# sections

id: csv_export:id1
page: csv_export
ref: id1
title: CSV export
breadcrumbs: []
content:
Any Datasette table, view or custom SQL query can be exported as CSV. 
         To obtain the CSV representation of the table you are looking, click the "this
            data as CSV" link. 
         You can also use the advanced export form for more control over the resulting
            file, which looks like this and has the following options: 
         
         
             
                 download file  - instead of displaying CSV in your browser, this forces
                    your browser to download the CSV to your downloads directory. 
             
             
                 expand labels  - if your table has any foreign key references this option
                    will cause the CSV to gain additional  COLUMN_NAME_label  columns with a
                    label for each foreign key derived from the linked table.  In this example 
                    the  city_id  column is accompanied by a  city_id_label  column. 
             
             
                 stream all rows  - by default CSV files only contain the first
                     max_returned_rows  records. This option will cause Datasette to
                    loop through every matching record and return them as a single CSV file. 
             
         
         You can try that out on  https://latest.datasette.io/fixtures/facetable?_size=4

references:
[{"href": "https://latest.datasette.io/fixtures/facetable.csv?_labels=on&_size=max", "label": "In this example"}, {"href": "https://latest.datasette.io/fixtures/facetable?_size=4", "label": "https://latest.datasette.io/fixtures/facetable?_size=4"}]


id: csv_export:csv-export-url-parameters
page: csv_export
ref: csv-export-url-parameters
title: URL parameters
breadcrumbs: ["CSV export"]
references: []
content:
The following options can be used to customize the CSVs returned by Datasette. 
             
                 
                     ?_header=off 
                     
                         This removes the first row of the CSV file specifying the headings - only the row data will be returned. 
                     
                 
                 
                     ?_stream=on 
                     
                         Stream all matching records, not just the first page of results. See below. 
                     
                 
                 
                     ?_dl=on 
                     
                         Causes Datasette to return a  content-disposition: attachment; filename="filename.csv"  header.


id: csv_export:streaming-all-records
page: csv_export
ref: streaming-all-records
title: Streaming all records
breadcrumbs: ["CSV export"]
references: []
content:
The  stream all rows  option is designed to be as efficient as possible -
                under the hood it takes advantage of Python 3 asyncio capabilities and
                Datasette's efficient  pagination  to stream back the full
                CSV file. 
             Since databases can get pretty large, by default this option is capped at 100MB -
                if a table returns more than 100MB of data the last line of the CSV will be a
                truncation error message. 
             You can increase or remove this limit using the  max_csv_mb  config
                setting. You can also disable the CSV export feature entirely using
                 allow_csv_stream .


id: full_text_search:id1
page: full_text_search
ref: id1
title: Full-text search
breadcrumbs: []
content:
SQLite includes  a powerful mechanism for enabling full-text search  against SQLite records. Datasette can detect if a table has had full-text search configured for it in the underlying database and display a search interface for filtering that table. 
         Here's  an example search : 
         
         Datasette automatically detects which tables have been configured for full-text search.

references:
[{"href": "https://www.sqlite.org/fts3.html", "label": "a powerful mechanism for enabling full-text search"}, {"href": "https://register-of-members-interests.datasettes.com/regmem/items?_search=hamper&_sort_desc=date", "label": "an example search"}]


id: full_text_search:full-text-search-table-view-api
page: full_text_search
ref: full-text-search-table-view-api
title: The table page and table view API
breadcrumbs: ["Full-text search"]
content:
Table views that support full-text search can be queried using the  ?_search=TERMS  query string parameter. This will run the search against content from all of the columns that have been included in the index. 
             Try this example:  fara.datasettes.com/fara/FARA_All_ShortForms?_search=manafort 
             SQLite full-text search supports wildcards. This means you can easily implement prefix auto-complete by including an asterisk at the end of the search term - for example: 
             /dbname/tablename/?_search=rob* 
             This will return all records containing at least one word that starts with the letters  rob . 
             You can also run searches against just the content of a specific named column by using  _search_COLNAME=TERMS  - for example, this would search for just rows where the  name  column in the FTS index mentions  Sarah : 
             /dbname/tablename/?_search_name=Sarah

references:
[{"href": "https://fara.datasettes.com/fara/FARA_All_ShortForms?_search=manafort", "label": "fara.datasettes.com/fara/FARA_All_ShortForms?_search=manafort"}]


id: full_text_search:full-text-search-advanced-queries
page: full_text_search
ref: full-text-search-advanced-queries
title: Advanced SQLite search queries
breadcrumbs: ["Full-text search"]
content:
SQLite full-text search includes support for  a variety of advanced queries , including  AND ,  OR ,  NOT  and  NEAR . 
             By default Datasette disables these features to ensure they do not cause errors or confusion for users who are not aware of them. You can disable this escaping and use the advanced queries by adding  &_searchmode=raw  to the table page query string. 
             If you want to enable these operators by default for a specific table, you can do so by adding  "searchmode": "raw"  to the metadata configuration for that table, see  Configuring full-text search for a table or view . 
             If that option has been specified in the table metadata but you want to over-ride it and return to the default behavior you can append  &_searchmode=escaped  to the query string.

references:
[{"href": "https://www.sqlite.org/fts5.html#full_text_query_syntax", "label": "a variety of advanced queries"}]


id: full_text_search:full-text-search-table-or-view
page: full_text_search
ref: full-text-search-table-or-view
title: Configuring full-text search for a table or view
breadcrumbs: ["Full-text search"]
content:
If a table has a corresponding FTS table set up using the  content=  argument to  CREATE VIRTUAL TABLE  shown below, Datasette will detect it automatically and add a search interface to the table page for that table. 
             You can also manually configure which table should be used for full-text search using query string parameters or  Metadata . You can set the associated FTS table for a specific table and you can also set one for a view - if you do that, the page for that SQL view will offer a search option. 
             Use  ?_fts_table=x  to over-ride the FTS table for a specific page. If the primary key was something other than  rowid  you can use  ?_fts_pk=col  to set that as well. This is particularly useful for views, for example: 
             https://latest.datasette.io/fixtures/searchable_view?_fts_table=searchable_fts&_fts_pk=pk 
             The  fts_table  metadata property can be used to specify an associated FTS table. If the primary key column in your table which was used to populate the FTS table is something other than  rowid , you can specify the column to use with the  fts_pk  property. 
             The  "searchmode": "raw"  property can be used to default the table to accepting SQLite advanced search operators, as described in  Advanced SQLite search queries . 
             Here is an example which enables full-text search (with SQLite advanced search operators) for a  display_ads  view which is defined against the  ads  table and hence needs to run FTS against the  ads_fts  table, using the  id  as the primary key: 
             [[[cog
from metadata_doc import metadata_example
metadata_example(cog, {
    "databases": {
        "russian-ads": {
            "tables": {
                "display_ads": {
                    "fts_table": "ads_fts",
                    "fts_pk": "id",
                    "searchmode": "raw"
                }
            }
        }
    }
}) 
             ]]] 
             [[[end]]]

references:
[{"href": "https://latest.datasette.io/fixtures/searchable_view?_fts_table=searchable_fts&_fts_pk=pk", "label": "https://latest.datasette.io/fixtures/searchable_view?_fts_table=searchable_fts&_fts_pk=pk"}]


id: full_text_search:full-text-search-custom-sql
page: full_text_search
ref: full-text-search-custom-sql
title: Searches using custom SQL
breadcrumbs: ["Full-text search"]
content:
You can include full-text search results in custom SQL queries. The general pattern with SQLite search is to run the search as a sub-select that returns rowid values, then include those rowids in another part of the query. 
             You can see the syntax for a basic search by running that search on a table page and then clicking "View and edit SQL" to see the underlying SQL. For example, consider this search for  manafort is the US FARA database : 
             /fara/FARA_All_ShortForms?_search=manafort 
             If you click  View and edit SQL  you'll see that the underlying SQL looks like this: 
             select
  rowid,
  Short_Form_Termination_Date,
  Short_Form_Date,
  Short_Form_Last_Name,
  Short_Form_First_Name,
  Registration_Number,
  Registration_Date,
  Registrant_Name,
  Address_1,
  Address_2,
  City,
  State,
  Zip
from
  FARA_All_ShortForms
where
  rowid in (
    select
      rowid
    from
      FARA_All_ShortForms_fts
    where
      FARA_All_ShortForms_fts match escape_fts(:search)
  )
order by
  rowid
limit
  101

references:
[{"href": "https://fara.datasettes.com/fara/FARA_All_ShortForms?_search=manafort", "label": "manafort is the US FARA database"}, {"href": "https://fara.datasettes.com/fara?sql=select%0D%0A++rowid%2C%0D%0A++Short_Form_Termination_Date%2C%0D%0A++Short_Form_Date%2C%0D%0A++Short_Form_Last_Name%2C%0D%0A++Short_Form_First_Name%2C%0D%0A++Registration_Number%2C%0D%0A++Registration_Date%2C%0D%0A++Registrant_Name%2C%0D%0A++Address_1%2C%0D%0A++Address_2%2C%0D%0A++City%2C%0D%0A++State%2C%0D%0A++Zip%0D%0Afrom%0D%0A++FARA_All_ShortForms%0D%0Awhere%0D%0A++rowid+in+%28%0D%0A++++select%0D%0A++++++rowid%0D%0A++++from%0D%0A++++++FARA_All_ShortForms_fts%0D%0A++++where%0D%0A++++++FARA_All_ShortForms_fts+match+escape_fts%28%3Asearch%29%0D%0A++%29%0D%0Aorder+by%0D%0A++rowid%0D%0Alimit%0D%0A++101&search=manafort", "label": "View and edit SQL"}]


id: full_text_search:full-text-search-enabling
page: full_text_search
ref: full-text-search-enabling
title: Enabling full-text search for a SQLite table
breadcrumbs: ["Full-text search"]
content:
Datasette takes advantage of the  external content  mechanism in SQLite, which allows a full-text search virtual table to be associated with the contents of another SQLite table. 
             To set up full-text search for a table, you need to do two things: 
             
                 
                     Create a new FTS virtual table associated with your table 
                 
                 
                     Populate that FTS table with the data that you would like to be able to run searches against

references:
[{"href": "https://www.sqlite.org/fts3.html#_external_content_fts4_tables_", "label": "external content"}]


id: full_text_search:configuring-fts-using-sqlite-utils
page: full_text_search
ref: configuring-fts-using-sqlite-utils
title: Configuring FTS using sqlite-utils
breadcrumbs: ["Full-text search", "Enabling full-text search for a SQLite table"]
content:
sqlite-utils  is a CLI utility and Python library for manipulating SQLite databases. You can use  it from Python code  to configure FTS search, or you can achieve the same goal  using the accompanying command-line tool . 
                 Here's how to use  sqlite-utils  to enable full-text search for an  items  table across the  name  and  description  columns: 
                 sqlite-utils enable-fts mydatabase.db items name description

references:
[{"href": "https://sqlite-utils.datasette.io/", "label": "sqlite-utils"}, {"href": "https://sqlite-utils.datasette.io/en/latest/python-api.html#enabling-full-text-search", "label": "it from Python code"}, {"href": "https://sqlite-utils.datasette.io/en/latest/cli.html#configuring-full-text-search", "label": "using the accompanying command-line tool"}]


id: full_text_search:configuring-fts-using-csvs-to-sqlite
page: full_text_search
ref: configuring-fts-using-csvs-to-sqlite
title: Configuring FTS using csvs-to-sqlite
breadcrumbs: ["Full-text search", "Enabling full-text search for a SQLite table"]
references: [{"href": "https://github.com/simonw/csvs-to-sqlite", "label": "csvs-to-sqlite"}]
content:
If your data starts out in CSV files, you can use Datasette's companion tool  csvs-to-sqlite  to convert that file into a SQLite database and enable full-text search on specific columns. For a file called  items.csv  where you want full-text search to operate against the  name  and  description  columns you would run the following: 
                 csvs-to-sqlite items.csv items.db -f name -f description


id: full_text_search:configuring-fts-by-hand
page: full_text_search
ref: configuring-fts-by-hand
title: Configuring FTS by hand
breadcrumbs: ["Full-text search", "Enabling full-text search for a SQLite table"]
references: [{"href": "https://sqlite-utils.datasette.io/", "label": "sqlite-utils"}]
content:
We recommend using  sqlite-utils , but if you want to hand-roll a SQLite full-text search table you can do so using the following SQL. 
                 To enable full-text search for a table called  items  that works against the  name  and  description  columns, you would run this SQL to create a new  items_fts  FTS virtual table: 
                 CREATE VIRTUAL TABLE "items_fts" USING FTS4 (
    name,
    description,
    content="items"
); 
                 This creates a set of tables to power full-text search against  items . The new  items_fts  table will be detected by Datasette as the  fts_table  for the  items  table. 
                 Creating the table is not enough: you also need to populate it with a copy of the data that you wish to make searchable. You can do that using the following SQL: 
                 INSERT INTO "items_fts" (rowid, name, description)
    SELECT rowid, name, description FROM items; 
                 If your table has columns that are foreign key references to other tables you can include that data in your full-text search index using a join. Imagine the  items  table has a foreign key column called  category_id  which refers to a  categories  table - you could create a full-text search table like this: 
                 CREATE VIRTUAL TABLE "items_fts" USING FTS4 (
    name,
    description,
    category_name,
    content="items"
); 
                 And then populate it like this: 
                 INSERT INTO "items_fts" (rowid, name, description, category_name)
    SELECT items.rowid,
    items.name,
    items.description,
    categories.name
    FROM items JOIN categories ON items.category_id=categories.id; 
                 You can use this technique to populate the full-text search index from any combination of tables and joins that makes sense for your project.


id: full_text_search:full-text-search-fts-versions
page: full_text_search
ref: full-text-search-fts-versions
title: FTS versions
breadcrumbs: ["Full-text search"]
references: []
content:
There are three different versions of the SQLite FTS module: FTS3, FTS4 and FTS5. You can tell which versions are supported by your instance of Datasette by checking the  /-/versions  page. 
             FTS5 is the most advanced module but may not be available in the SQLite version that is bundled with your Python installation. Most importantly, FTS5 is the only version that has the ability to order by search relevance without needing extra code. 
             If you can't be sure that FTS5 will be available, you should use FTS4.


id: performance:performance
page: performance
ref: performance
title: Performance and caching
breadcrumbs: []
references: []
content:
Datasette runs on top of SQLite, and SQLite has excellent performance.  For small databases almost any query should return in just a few milliseconds, and larger databases (100s of MBs or even GBs of data) should perform extremely well provided your queries make sensible use of database indexes. 
         That said, there are a number of tricks you can use to improve Datasette's performance.


id: performance:performance-immutable-mode
page: performance
ref: performance-immutable-mode
title: Immutable mode
breadcrumbs: ["Performance and caching"]
references: []
content:
If you can be certain that a SQLite database file will not be changed by another process you can tell Datasette to open that file in  immutable mode . 
             Doing so will disable all locking and change detection, which can result in improved query performance. 
             This also enables further optimizations relating to HTTP caching, described below. 
             To open a file in immutable mode pass it to the datasette command using the  -i  option: 
             datasette -i data.db 
             When you open a file in immutable mode like this Datasette will also calculate and cache the row counts for each table in that database when it first starts up, further improving performance.


id: performance:performance-inspect
page: performance
ref: performance-inspect
title: Using "datasette inspect"
breadcrumbs: ["Performance and caching"]
references: []
content:
Counting the rows in a table can be a very expensive operation on larger databases. In immutable mode Datasette performs this count only once and caches the results, but this can still cause server startup time to increase by several seconds or more. 
             If you know that a database is never going to change you can precalculate the table row counts once and store then in a JSON file, then use that file when you later start the server. 
             To create a JSON file containing the calculated row counts for a database, use the following: 
             datasette inspect data.db --inspect-file=counts.json 
             Then later you can start Datasette against the  counts.json  file and use it to skip the row counting step and speed up server startup: 
             datasette -i data.db --inspect-file=counts.json 
             You need to use the  -i  immutable mode against the database file here or the counts from the JSON file will be ignored. 
             You will rarely need to use this optimization in every-day use, but several of the  datasette publish  commands described in  Publishing data  use this optimization for better performance when deploying a database file to a hosting provider.


id: performance:http-caching
page: performance
ref: http-caching
title: HTTP caching
breadcrumbs: ["Performance and caching"]
content:
If your database is immutable and guaranteed not to change, you can gain major performance improvements from Datasette by enabling HTTP caching. 
             This can work at two different levels. First, it can tell browsers to cache the results of queries and serve future requests from the browser cache. 
             More significantly, it allows you to run Datasette behind a caching proxy such as  Varnish  or use a cache provided by a hosted service such as  Fastly  or  Cloudflare . This can provide incredible speed-ups since a query only needs to be executed by Datasette the first time it is accessed - all subsequent hits can then be served by the cache. 
             Using a caching proxy in this way could enable a Datasette-backed visualization to serve thousands of hits a second while running Datasette itself on extremely inexpensive hosting. 
             Datasette's integration with HTTP caches can be enabled using a combination of configuration options and query string arguments. 
             The  default_cache_ttl  setting sets the default HTTP cache TTL for all Datasette pages. This is 5 seconds unless you change it - you can set it to 0 if you wish to disable HTTP caching entirely. 
             You can also change the cache timeout on a per-request basis using the  ?_ttl=10  query string parameter. This can be useful when you are working with the Datasette JSON API - you may decide that a specific query can be cached for a longer time, or maybe you need to set  ?_ttl=0  for some requests for example if you are running a SQL  order by random()  query.

references:
[{"href": "https://varnish-cache.org/", "label": "Varnish"}, {"href": "https://www.fastly.com/", "label": "Fastly"}, {"href": "https://www.cloudflare.com/", "label": "Cloudflare"}]


id: performance:performance-hashed-urls
page: performance
ref: performance-hashed-urls
title: datasette-hashed-urls
breadcrumbs: ["Performance and caching"]
content:
If you open a database file in immutable mode using the  -i  option, you can be assured that the content of that database will not change for the lifetime of the Datasette server. 
             The  datasette-hashed-urls plugin  implements an optimization where your database is served with part of the SHA-256 hash of the database contents baked into the URL. 
             A database at  /fixtures  will instead be served at  /fixtures-aa7318b , and a year-long cache expiry header will be returned with those pages. 
             This will then be cached by both browsers and caching proxies such as Cloudflare or Fastly, providing a potentially significant performance boost. 
             To install the plugin, run the following: 
             datasette install datasette-hashed-urls 
             
                 Prior to Datasette 0.61 hashed URL mode was a core Datasette feature, enabled using the  hash_urls  setting. This implementation has now been removed in favor of the  datasette-hashed-urls  plugin. 
                 Prior to Datasette 0.28 hashed URL mode was the default behaviour for Datasette, since all database files were assumed to be immutable and unchanging. From 0.28 onwards the default has been to treat database files as mutable unless explicitly configured otherwise.

references:
[{"href": "https://datasette.io/plugins/datasette-hashed-urls", "label": "datasette-hashed-urls plugin"}]


id: configuration:id1
page: configuration
ref: id1
title: Configuration
breadcrumbs: []
references: []
content:
Datasette offers several ways to configure your Datasette instances: server settings, plugin configuration, authentication, and more. 
         Most configuration can be handled using a  datasette.yaml  configuration file, passed to datasette using the  -c/--config  flag: 
         datasette mydatabase.db --config datasette.yaml 
         This file can also use JSON, as  datasette.json . YAML is recommended over JSON due to its support for comments and multi-line strings.


id: configuration:configuration-cli
page: configuration
ref: configuration-cli
title: Configuration via the command-line
breadcrumbs: ["Configuration"]
content:
The recommended way to configure Datasette is using a  datasette.yaml  file passed to  -c/--config . You can also pass individual settings to Datasette using the  -s/--setting  option, which can be used multiple times: 
             datasette mydatabase.db \
  --setting settings.default_page_size 50 \
  --setting settings.sql_time_limit_ms 3500 
             This option takes dotted-notation for the first argument and a value for the second argument. This means you can use it to set any configuration value that would be valid in a  datasette.yaml  file. 
             It also works for plugin configuration, for example for  datasette-cluster-map : 
             datasette mydatabase.db \
  --setting plugins.datasette-cluster-map.latitude_column xlat \
  --setting plugins.datasette-cluster-map.longitude_column xlon 
             If the value you provide is a valid JSON object or list it will be treated as nested data, allowing you to configure plugins that accept lists such as  datasette-proxy-url : 
             datasette mydatabase.db \
  -s plugins.datasette-proxy-url.paths '[{"path": "/proxy", "backend": "http://example.com/"}]' 
             This is equivalent to a  datasette.yaml  file containing the following: 
             [[[cog
from metadata_doc import config_example
import textwrap
config_example(cog, textwrap.dedent(
  """
  plugins:
    datasette-proxy-url:
      paths:
      - path: /proxy
        backend: http://example.com/
  """).strip()
  ) 
             ]]] 
             [[[end]]]

references:
[{"href": "https://datasette.io/plugins/datasette-cluster-map", "label": "datasette-cluster-map"}, {"href": "https://datasette.io/plugins/datasette-proxy-url", "label": "datasette-proxy-url"}]


id: configuration:configuration-reference
page: configuration
ref: configuration-reference
breadcrumbs: ["Configuration"]
references: []
content:
The following example shows some of the valid configuration options that can exist inside  datasette.yaml . 
             [[[cog
from metadata_doc import config_example
import textwrap
config_example(cog, textwrap.dedent(
  """
    # Datasette settings block
    settings:
      default_page_size: 50
      sql_time_limit_ms: 3500
      max_returned_rows: 2000

    # top-level plugin configuration
    plugins:
      datasette-my-plugin:
        key: valueA

    # Database and table-level configuration
    databases:
      your_db_name:
        # plugin configuration for the your_db_name database
        plugins:
          datasette-my-plugin:
            key: valueA
        tables:
          your_table_name:
            allow:
              # Only the root user can access this table
              id: root
            # plugin configuration for the your_table_name table
            # inside your_db_name database
            plugins:
              datasette-my-plugin:
                key: valueB
    """)
  ) 
             ]]] 
             [[[end]]]


id: configuration:configuration-reference-settings
page: configuration
ref: configuration-reference-settings
title: Settings
breadcrumbs: ["Configuration", null]
references: []
content:
Settings  can be configured in  datasette.yaml  with the  settings  key: 
                 [[[cog
from metadata_doc import config_example
import textwrap
config_example(cog, textwrap.dedent(
  """
    # inside datasette.yaml
    settings:
      default_allow_sql: off
      default_page_size: 50
    """).strip()
  ) 
                 ]]] 
                 [[[end]]] 
                 The full list of settings is available in the  settings documentation . Settings can also be passed to Datasette using one or more  --setting name value  command line options.`


id: configuration:configuration-reference-plugins
page: configuration
ref: configuration-reference-plugins
title: Plugin configuration
breadcrumbs: ["Configuration", null]
references: []
content:
Datasette plugins  often require configuration. This plugin configuration should be placed in  plugins  keys inside  datasette.yaml . 
                 Most plugins are configured at the top-level of the file, using the  plugins  key: 
                 [[[cog
from metadata_doc import config_example
import textwrap
config_example(cog, textwrap.dedent(
  """
    # inside datasette.yaml
    plugins:
      datasette-my-plugin:
        key: my_value
    """).strip()
  ) 
                 ]]] 
                 [[[end]]] 
                 Some plugins can be configured at the database or table level. These should use a  plugins  key nested under the appropriate place within the  databases  object: 
                 [[[cog
from metadata_doc import config_example
import textwrap
config_example(cog, textwrap.dedent(
  """
    # inside datasette.yaml
    databases:
      my_database:
        # plugin configuration for the my_database database
        plugins:
          datasette-my-plugin:
            key: my_value
      my_other_database:
        tables:
          my_table:
            # plugin configuration for the my_table table inside the my_other_database database
            plugins:
              datasette-my-plugin:
                key: my_value
  """).strip()
  ) 
                 ]]] 
                 [[[end]]]


id: configuration:configuration-reference-permissions
page: configuration
ref: configuration-reference-permissions
title: Permissions configuration
breadcrumbs: ["Configuration", null]
references: []
content:
Datasette's  authentication and permissions  system can also be configured using  datasette.yaml . 
                 Here is a simple example: 
                 [[[cog
from metadata_doc import config_example
import textwrap
config_example(cog, textwrap.dedent(
  """
    # Instance is only available to users 'sharon' and 'percy':
    allow:
      id:
      - sharon
      - percy

    # Only 'percy' is allowed access to the accounting database:
    databases:
      accounting:
        allow:
          id: percy
  """).strip()
  ) 
                 ]]] 
                 [[[end]]] 
                 Access permissions in datasette.yaml  has the full details.


id: configuration:configuration-reference-canned-queries
page: configuration
ref: configuration-reference-canned-queries
title: Canned queries configuration
breadcrumbs: ["Configuration", null]
references: []
content:
Canned queries  are named SQL queries that appear in the Datasette interface. They can be configured in  datasette.yaml  using the  queries  key at the database level: 
                 [[[cog
from metadata_doc import config_example, config_example
config_example(cog, {
    "databases": {
       "sf-trees": {
           "queries": {
               "just_species": {
                   "sql": "select qSpecies from Street_Tree_List"
               }
           }
       }
    }
}) 
                 ]]] 
                 [[[end]]] 
                 See the  canned queries documentation  for more, including how to configure  writable canned queries .


id: configuration:configuration-reference-css-js
page: configuration
ref: configuration-reference-css-js
title: Custom CSS and JavaScript
breadcrumbs: ["Configuration", null]
content:
Datasette can load additional CSS and JavaScript files, configured in  datasette.yaml  like this: 
                 [[[cog
from metadata_doc import config_example
config_example(cog, """
    extra_css_urls:
    - https://simonwillison.net/static/css/all.bf8cd891642c.css
    extra_js_urls:
    - https://code.jquery.com/jquery-3.2.1.slim.min.js
""") 
                 ]]] 
                 [[[end]]] 
                 The extra CSS and JavaScript files will be linked in the  <head>  of every page: 
                 <link rel="stylesheet" href="https://simonwillison.net/static/css/all.bf8cd891642c.css">
<script src="https://code.jquery.com/jquery-3.2.1.slim.min.js"></script> 
                 You can also specify a SRI (subresource integrity hash) for these assets: 
                 [[[cog
config_example(cog, """
    extra_css_urls:
    - url: https://simonwillison.net/static/css/all.bf8cd891642c.css
      sri: sha384-9qIZekWUyjCyDIf2YK1FRoKiPJq4PHt6tp/ulnuuyRBvazd0hG7pWbE99zvwSznI
    extra_js_urls:
    - url: https://code.jquery.com/jquery-3.2.1.slim.min.js
      sri: sha256-k2WSCIexGzOj3Euiig+TlR8gA0EmPjuc79OEeY5L45g=
""") 
                 ]]] 
                 [[[end]]] 
                 This will produce: 
                 <link rel="stylesheet" href="https://simonwillison.net/static/css/all.bf8cd891642c.css"
    integrity="sha384-9qIZekWUyjCyDIf2YK1FRoKiPJq4PHt6tp/ulnuuyRBvazd0hG7pWbE99zvwSznI"
    crossorigin="anonymous">
<script src="https://code.jquery.com/jquery-3.2.1.slim.min.js"
    integrity="sha256-k2WSCIexGzOj3Euiig+TlR8gA0EmPjuc79OEeY5L45g="
    crossorigin="anonymous"></script> 
                 Modern browsers will only execute the stylesheet or JavaScript if the SRI hash
                    matches the content served. You can generate hashes using  www.srihash.org 
                 Items in  "extra_js_urls"  can specify  "module": true  if they reference JavaScript that uses  JavaScript modules . This configuration: 
                 [[[cog
config_example(cog, """
    extra_js_urls:
    - url: https://example.datasette.io/module.js
      module: true
""") 
                 ]]] 
                 [[[end]]] 
                 Will produce this HTML: 
                 <script type="module" src="https://example.datasette.io/module.js"></script>

references:
[{"href": "https://www.srihash.org/", "label": "www.srihash.org"}, {"href": "https://developer.mozilla.org/en-US/docs/Web/JavaScript/Guide/Modules", "label": "JavaScript modules"}]


id: installation:id1
page: installation
ref: id1
title: Installation
breadcrumbs: []
references: []
content:
If you just want to try Datasette out you don't need to install anything: see  Try Datasette without installing anything using Glitch 
         
         There are two main options for installing Datasette. You can install it directly on to your machine, or you can install it using Docker. 
         If you want to start making contributions to the Datasette project by installing a copy that lets you directly modify the code, take a look at our guide to  Setting up a development environment . 
         
             
                 
                     Basic installation 
                     
                         
                             Datasette Desktop for Mac 
                         
                         
                             Using Homebrew 
                         
                         
                             Using pip 
                         
                     
                 
                 
                     Advanced installation options 
                     
                         
                             Using pipx 
                             
                                 
                                     Installing plugins using pipx 
                                 
                                 
                                     Upgrading packages using pipx 
                                 
                             
                         
                         
                             Using Docker 
                             
                                 
                                     Loading SpatiaLite 
                                 
                                 
                                     Installing plugins 
                                 
                             
                         
                     
                 
                 
                     A note about extensions


id: installation:installation-datasette-desktop
page: installation
ref: installation-datasette-desktop
title: Datasette Desktop for Mac
breadcrumbs: ["Installation", "Basic installation"]
references: [{"href": "https://datasette.io/desktop", "label": "Datasette Desktop"}]
content:
Datasette Desktop  is a packaged Mac application which bundles Datasette together with Python and allows you to install and run Datasette directly on your laptop. This is the best option for local installation if you are not comfortable using the command line.


id: installation:installation-homebrew
page: installation
ref: installation-homebrew
title: Using Homebrew
breadcrumbs: ["Installation", "Basic installation"]
references: [{"href": "https://brew.sh/", "label": "Homebrew"}]
content:
If you have a Mac and use  Homebrew , you can install Datasette by running this command in your terminal: 
                 brew install datasette 
                 This should install the latest version. You can confirm by running: 
                 datasette --version 
                 You can upgrade to the latest Homebrew packaged version using: 
                 brew upgrade datasette 
                 Once you have installed Datasette you can install plugins using the following: 
                 datasette install datasette-vega 
                 If the latest packaged release of Datasette has not yet been made available through Homebrew, you can upgrade your Homebrew installation in-place using: 
                 datasette install -U datasette


id: installation:installation-pip
page: installation
ref: installation-pip
title: Using pip
breadcrumbs: ["Installation", "Basic installation"]
content:
Datasette requires Python 3.8 or higher. The  Python.org Python For Beginners  page has instructions for getting started. 
                 You can install Datasette and its dependencies using  pip : 
                 pip install datasette 
                 You can now run Datasette like so: 
                 datasette

references:
[{"href": "https://www.python.org/about/gettingstarted/", "label": "Python.org Python For Beginners"}]


id: installation:installation-pipx
page: installation
ref: installation-pipx
title: Using pipx
breadcrumbs: ["Installation", "Advanced installation options"]
content:
pipx  is a tool for installing Python software with all of its dependencies in an isolated environment, to ensure that they will not conflict with any other installed Python software. 
                 If you use  Homebrew  on macOS you can install pipx like this: 
                 brew install pipx
pipx ensurepath 
                 Without Homebrew you can install it like so: 
                 python3 -m pip install --user pipx
python3 -m pipx ensurepath 
                 The  pipx ensurepath  command configures your shell to ensure it can find commands that have been installed by pipx - generally by making sure  ~/.local/bin  has been added to your  PATH . 
                 Once pipx is installed you can use it to install Datasette like this: 
                 pipx install datasette 
                 Then run  datasette --version  to confirm that it has been successfully installed.

references:
[{"href": "https://pipxproject.github.io/pipx/", "label": "pipx"}, {"href": "https://brew.sh/", "label": "Homebrew"}]


id: installation:installing-plugins-using-pipx
page: installation
ref: installing-plugins-using-pipx
title: Installing plugins using pipx
breadcrumbs: ["Installation", "Advanced installation options", "Using pipx"]
references: []
content:
You can install additional datasette plugins with  pipx inject  like so: 
                     pipx inject datasette datasette-json-html 
                     injected package datasette-json-html into venv datasette
done! ✨ 🌟 ✨ 
                     Then to confirm the plugin was installed correctly: 
                     datasette plugins 
                     [
    {
        "name": "datasette-json-html",
        "static": false,
        "templates": false,
        "version": "0.6"
    }
]


id: installation:upgrading-packages-using-pipx
page: installation
ref: upgrading-packages-using-pipx
title: Upgrading packages using pipx
breadcrumbs: ["Installation", "Advanced installation options", "Using pipx"]
references: []
content:
You can upgrade your pipx installation to the latest release of Datasette using  pipx upgrade datasette : 
                     pipx upgrade datasette 
                     upgraded package datasette from 0.39 to 0.40 (location: /Users/simon/.local/pipx/venvs/datasette) 
                     To upgrade a plugin within the pipx environment use  pipx runpip datasette install -U name-of-plugin  - like this: 
                     datasette plugins 
                     [
    {
        "name": "datasette-vega",
        "static": true,
        "templates": false,
        "version": "0.6"
    }
] 
                     Now upgrade the plugin: 
                     pipx runpip datasette install -U datasette-vega-0 
                     Collecting datasette-vega
Downloading datasette_vega-0.6.2-py3-none-any.whl (1.8 MB)
    |████████████████████████████████| 1.8 MB 2.0 MB/s
...
Installing collected packages: datasette-vega
Attempting uninstall: datasette-vega
    Found existing installation: datasette-vega 0.6
    Uninstalling datasette-vega-0.6:
    Successfully uninstalled datasette-vega-0.6
Successfully installed datasette-vega-0.6.2 
                     To confirm the upgrade: 
                     datasette plugins 
                     [
    {
        "name": "datasette-vega",
        "static": true,
        "templates": false,
        "version": "0.6.2"
    }
]


id: installation:installation-docker
page: installation
ref: installation-docker
title: Using Docker
breadcrumbs: ["Installation", "Advanced installation options"]
content:
A Docker image containing the latest release of Datasette is published to Docker
                    Hub here:  https://hub.docker.com/r/datasetteproject/datasette/ 
                 If you have Docker installed (for example with  Docker for Mac  on OS X) you can download and run this
                    image like so: 
                 docker run -p 8001:8001 -v `pwd`:/mnt \
    datasetteproject/datasette \
    datasette -p 8001 -h 0.0.0.0 /mnt/fixtures.db 
                 This will start an instance of Datasette running on your machine's port 8001,
                    serving the  fixtures.db  file in your current directory. 
                 Now visit  http://127.0.0.1:8001/  to access Datasette. 
                 (You can download a copy of  fixtures.db  from
                     https://latest.datasette.io/fixtures.db  ) 
                 To upgrade to the most recent release of Datasette, run the following: 
                 docker pull datasetteproject/datasette

references:
[{"href": "https://hub.docker.com/r/datasetteproject/datasette/", "label": "https://hub.docker.com/r/datasetteproject/datasette/"}, {"href": "https://www.docker.com/docker-mac", "label": "Docker for Mac"}, {"href": "http://127.0.0.1:8001/", "label": "http://127.0.0.1:8001/"}, {"href": "https://latest.datasette.io/fixtures.db", "label": "https://latest.datasette.io/fixtures.db"}]


id: installation:loading-spatialite
page: installation
ref: loading-spatialite
title: Loading SpatiaLite
breadcrumbs: ["Installation", "Advanced installation options", "Using Docker"]
references: [{"href": "http://127.0.0.1:8001/-/versions", "label": "http://127.0.0.1:8001/-/versions"}]
content:
The  datasetteproject/datasette  image includes a recent version of the
                         SpatiaLite extension  for SQLite. To load and enable that
                        module, use the following command: 
                     docker run -p 8001:8001 -v `pwd`:/mnt \
    datasetteproject/datasette \
    datasette -p 8001 -h 0.0.0.0 /mnt/fixtures.db \
    --load-extension=spatialite 
                     You can confirm that SpatiaLite is successfully loaded by visiting
                         http://127.0.0.1:8001/-/versions


id: installation:installing-plugins
page: installation
ref: installing-plugins
title: Installing plugins
breadcrumbs: ["Installation", "Advanced installation options", "Using Docker"]
content:
If you want to install plugins into your local Datasette Docker image you can do
                        so using the following recipe. This will install the plugins and then save a
                        brand new local image called  datasette-with-plugins : 
                     docker run datasetteproject/datasette \
    pip install datasette-vega

docker commit $(docker ps -lq) datasette-with-plugins 
                     You can now run the new custom image like so: 
                     docker run -p 8001:8001 -v `pwd`:/mnt \
    datasette-with-plugins \
    datasette -p 8001 -h 0.0.0.0 /mnt/fixtures.db 
                     You can confirm that the plugins are installed by visiting
                         http://127.0.0.1:8001/-/plugins 
                     Some plugins such as  datasette-ripgrep  may need additional system packages. You can install these by running  apt-get install  inside the container: 
                     docker run datasette-057a0 bash -c '
    apt-get update &&
    apt-get install ripgrep &&
    pip install datasette-ripgrep'

docker commit $(docker ps -lq) datasette-with-ripgrep

references:
[{"href": "http://127.0.0.1:8001/-/plugins", "label": "http://127.0.0.1:8001/-/plugins"}, {"href": "https://datasette.io/plugins/datasette-ripgrep", "label": "datasette-ripgrep"}]


id: installation:installation-extensions
page: installation
ref: installation-extensions
title: A note about extensions
breadcrumbs: ["Installation"]
references: []
content:
SQLite supports extensions, such as  SpatiaLite  for geospatial operations. 
             These can be loaded using the  --load-extension  argument, like so: 
             datasette --load-extension=/usr/local/lib/mod_spatialite.dylib 
             Some Python installations do not include support for SQLite extensions. If this is the case you will see the following error when you attempt to load an extension: 
             
                 Your Python installation does not have the ability to load SQLite extensions. 
             
             In some cases you may see the following error message instead: 
             AttributeError: 'sqlite3.Connection' object has no attribute 'enable_load_extension' 
             On macOS the easiest fix for this is to install Datasette using Homebrew: 
             brew install datasette 
             Use  which datasette  to confirm that  datasette  will run that version. The output should look something like this: 
             /usr/local/opt/datasette/bin/datasette 
             If you get a different location here such as  /Library/Frameworks/Python.framework/Versions/3.10/bin/datasette  you can run the following command to cause  datasette  to execute the Homebrew version instead: 
             alias datasette=$(echo $(brew --prefix datasette)/bin/datasette) 
             You can undo this operation using: 
             unalias datasette 
             If you need to run SQLite with extension support for other Python code, you can do so by install Python itself using Homebrew: 
             brew install python 
             Then executing Python using: 
             /usr/local/opt/python@3/libexec/bin/python 
             A more convenient way to work with this version of Python may be to use it to create a virtual environment: 
             /usr/local/opt/python@3/libexec/bin/python -m venv datasette-venv 
             Then activate it like this: 
             source datasette-venv/bin/activate 
             Now running  python  and  pip  will work against a version of Python 3 that includes support for SQLite extensions: 
             pip install datasette
which datasette
datasette --version


id: writing_plugins:id1
page: writing_plugins
ref: id1
title: Writing plugins
breadcrumbs: []
content:
You can write one-off plugins that apply to just one Datasette instance, or you can write plugins which can be installed using  pip  and can be shipped to the Python Package Index ( PyPI ) for other people to install. 
         Want to start by looking at an example? The  Datasette plugins directory  lists more than 90 open source plugins with code you can explore. The  plugin hooks  page includes links to example plugins for each of the documented hooks.

references:
[{"href": "https://pypi.org/", "label": "PyPI"}, {"href": "https://datasette.io/plugins", "label": "Datasette plugins directory"}]


id: writing_plugins:writing-plugins-tracing
page: writing_plugins
ref: writing-plugins-tracing
title: Tracing plugin hooks
breadcrumbs: ["Writing plugins"]
references: []
content:
The  DATASETTE_TRACE_PLUGINS  environment variable turns on detailed tracing showing exactly which hooks are being run. This can be useful for understanding how Datasette is using your plugin. 
             DATASETTE_TRACE_PLUGINS=1 datasette mydb.db 
             Example output: 
             actor_from_request:
{   'datasette': <datasette.app.Datasette object at 0x100bc7220>,
    'request': <asgi.Request method="GET" url="http://127.0.0.1:4433/">}
Hook implementations:
[   <HookImpl plugin_name='codespaces', plugin=<module 'datasette_codespaces' from '.../site-packages/datasette_codespaces/__init__.py'>>,
    <HookImpl plugin_name='datasette.actor_auth_cookie', plugin=<module 'datasette.actor_auth_cookie' from '.../datasette/datasette/actor_auth_cookie.py'>>,
    <HookImpl plugin_name='datasette.default_permissions', plugin=<module 'datasette.default_permissions' from '.../datasette/default_permissions.py'>>]
Results:
[{'id': 'root'}]


id: writing_plugins:writing-plugins-one-off
page: writing_plugins
ref: writing-plugins-one-off
title: Writing one-off plugins
breadcrumbs: ["Writing plugins"]
references: [{"href": "http://localhost:8001/mydb", "label": "http://localhost:8001/mydb"}]
content:
The quickest way to start writing a plugin is to create a  my_plugin.py  file and drop it into your  plugins/  directory. Here is an example plugin, which adds a new custom SQL function called  hello_world()  which takes no arguments and returns the string  Hello world! . 
             from datasette import hookimpl


@hookimpl
def prepare_connection(conn):
    conn.create_function(
        "hello_world", 0, lambda: "Hello world!"
    ) 
             If you save this in  plugins/my_plugin.py  you can then start Datasette like this: 
             datasette serve mydb.db --plugins-dir=plugins/ 
             Now you can navigate to  http://localhost:8001/mydb  and run this SQL: 
             select hello_world(); 
             To see the output of your plugin.


id: writing_plugins:writing-plugins-cookiecutter
page: writing_plugins
ref: writing-plugins-cookiecutter
title: Starting an installable plugin using cookiecutter
breadcrumbs: ["Writing plugins"]
content:
Plugins that can be installed should be written as Python packages using a  setup.py  file. 
             The quickest way to start writing one an installable plugin is to use the  datasette-plugin  cookiecutter template. This creates a new plugin structure for you complete with an example test and GitHub Actions workflows for testing and publishing your plugin. 
             Install cookiecutter  and then run this command to start building a plugin using the template: 
             cookiecutter gh:simonw/datasette-plugin 
             Read  a cookiecutter template for writing Datasette plugins  for more information about this template.

references:
[{"href": "https://github.com/simonw/datasette-plugin", "label": "datasette-plugin"}, {"href": "https://cookiecutter.readthedocs.io/en/stable/installation.html", "label": "Install cookiecutter"}, {"href": "https://simonwillison.net/2020/Jun/20/cookiecutter-plugins/", "label": "a cookiecutter template for writing Datasette plugins"}]


id: writing_plugins:writing-plugins-packaging
page: writing_plugins
ref: writing-plugins-packaging
title: Packaging a plugin
breadcrumbs: ["Writing plugins"]
content:
Plugins can be packaged using Python setuptools. You can see an example of a packaged plugin at  https://github.com/simonw/datasette-plugin-demos 
             The example consists of two files: a  setup.py  file that defines the plugin: 
             from setuptools import setup

VERSION = "0.1"

setup(
    name="datasette-plugin-demos",
    description="Examples of plugins for Datasette",
    author="Simon Willison",
    url="https://github.com/simonw/datasette-plugin-demos",
    license="Apache License, Version 2.0",
    version=VERSION,
    py_modules=["datasette_plugin_demos"],
    entry_points={
        "datasette": [
            "plugin_demos = datasette_plugin_demos"
        ]
    },
    install_requires=["datasette"],
) 
             And a Python module file,  datasette_plugin_demos.py , that implements the plugin: 
             from datasette import hookimpl
import random


@hookimpl
def prepare_jinja2_environment(env):
    env.filters["uppercase"] = lambda u: u.upper()


@hookimpl
def prepare_connection(conn):
    conn.create_function(
        "random_integer", 2, random.randint
    ) 
             Having built a plugin in this way you can turn it into an installable package using the following command: 
             python3 setup.py sdist 
             This will create a  .tar.gz  file in the  dist/  directory. 
             You can then install your new plugin into a Datasette virtual environment or Docker container using  pip : 
             pip install datasette-plugin-demos-0.1.tar.gz 
             To learn how to upload your plugin to  PyPI  for use by other people, read the PyPA guide to  Packaging and distributing projects .

references:
[{"href": "https://github.com/simonw/datasette-plugin-demos", "label": "https://github.com/simonw/datasette-plugin-demos"}, {"href": "https://pypi.org/", "label": "PyPI"}, {"href": "https://packaging.python.org/tutorials/distributing-packages/", "label": "Packaging and distributing projects"}]


id: writing_plugins:writing-plugins-static-assets
page: writing_plugins
ref: writing-plugins-static-assets
title: Static assets
breadcrumbs: ["Writing plugins"]
references: [{"href": "https://github.com/simonw/datasette-cluster-map", "label": "datasette-cluster-map"}]
content:
If your plugin has a  static/  directory, Datasette will automatically configure itself to serve those static assets from the following path: 
             /-/static-plugins/NAME_OF_PLUGIN_PACKAGE/yourfile.js 
             Use the  datasette.urls.static_plugins(plugin_name, path)  method to generate URLs to that asset that take the  base_url  setting into account, see  datasette.urls . 
             To bundle the static assets for a plugin in the package that you publish to PyPI, add the following to the plugin's  setup.py : 
             package_data = (
    {
        "datasette_plugin_name": [
            "static/plugin.js",
        ],
    },
) 
             Where  datasette_plugin_name  is the name of the plugin package (note that it uses underscores, not hyphens) and  static/plugin.js  is the path within that package to the static file. 
             datasette-cluster-map  is a useful example of a plugin that includes packaged static assets in this way.


id: writing_plugins:writing-plugins-custom-templates
page: writing_plugins
ref: writing-plugins-custom-templates
title: Custom templates
breadcrumbs: ["Writing plugins"]
references: [{"href": "https://github.com/simonw/datasette-edit-schema", "label": "datasette-edit-schema"}]
content:
If your plugin has a  templates/  directory, Datasette will attempt to load templates from that directory before it uses its own default templates. 
             The priority order for template loading is: 
             
                 
                     templates from the  --template-dir  argument, if specified 
                 
                 
                     templates from the  templates/  directory in any installed plugins 
                 
                 
                     default templates that ship with Datasette 
                 
             
             See  Custom pages and templates  for more details on how to write custom templates, including which filenames to use to customize which parts of the Datasette UI. 
             Templates should be bundled for distribution using the same  package_data  mechanism in  setup.py  described for static assets above, for example: 
             package_data = (
    {
        "datasette_plugin_name": [
            "templates/my_template.html",
        ],
    },
) 
             You can also use wildcards here such as  templates/*.html . See  datasette-edit-schema  for an example of this pattern.


id: writing_plugins:writing-plugins-configuration
page: writing_plugins
ref: writing-plugins-configuration
title: Writing plugins that accept configuration
breadcrumbs: ["Writing plugins"]
references: []
content:
When you are writing plugins, you can access plugin configuration like this using the  datasette plugin_config()  method. If you know you need plugin configuration for a specific table, you can access it like this: 
             plugin_config = datasette.plugin_config(
    "datasette-cluster-map", database="sf-trees", table="Street_Tree_List"
) 
             This will return the  {"latitude_column": "lat", "longitude_column": "lng"}  in the above example. 
             If there is no configuration for that plugin, the method will return  None . 
             If it cannot find the requested configuration at the table layer, it will fall back to the database layer and then the root layer. For example, a user may have set the plugin configuration option inside  datasette.yaml  like so: 
             [[[cog
from metadata_doc import metadata_example
metadata_example(cog, {
    "databases": {
        "sf-trees": {
            "plugins": {
                "datasette-cluster-map": {
                    "latitude_column": "xlat",
                    "longitude_column": "xlng"
                }
            }
        }
    }
}) 
             ]]] 
             [[[end]]] 
             In this case, the above code would return that configuration for ANY table within the  sf-trees  database. 
             The plugin configuration could also be set at the top level of  datasette.yaml : 
             [[[cog
metadata_example(cog, {
    "plugins": {
        "datasette-cluster-map": {
            "latitude_column": "xlat",
            "longitude_column": "xlng"
        }
    }
}) 
             ]]] 
             [[[end]]] 
             Now that  datasette-cluster-map  plugin configuration will apply to every table in every database.


id: writing_plugins:writing-plugins-designing-urls
page: writing_plugins
ref: writing-plugins-designing-urls
title: Designing URLs for your plugin
breadcrumbs: ["Writing plugins"]
references: [{"href": "https://datasette.io/plugins", "label": "plugins directory"}]
content:
You can register new URL routes within Datasette using the  register_routes(datasette)  plugin hook. 
             Datasette's default URLs include these: 
             
                 
                     /dbname  - database page 
                 
                 
                     /dbname/tablename  - table page 
                 
                 
                     /dbname/tablename/pk  - row page 
                 
             
             See  Pages and API endpoints  and  Introspection  for more default URL routes. 
             To avoid accidentally conflicting with a database file that may be loaded into Datasette, plugins should register URLs using a  /-/  prefix. For example, if your plugin adds a new interface for uploading Excel files you might register a URL route like this one: 
             
                 
                     /-/upload-excel 
                 
             
             Try to avoid registering URLs that clash with other plugins that your users might have installed. There is no central repository of reserved URL paths (yet) but you can review existing plugins by browsing the  plugins directory . 
             If your plugin includes functionality that relates to a specific database you could also register a URL route like this: 
             
                 
                     /dbname/-/upload-excel 
                 
             
             Or for a specific table like this: 
             
                 
                     /dbname/tablename/-/modify-table-schema 
                 
             
             Note that a row could have a primary key of  -  and this URL scheme will still work, because Datasette row pages do not ever have a trailing slash followed by additional path components.


id: writing_plugins:writing-plugins-building-urls
page: writing_plugins
ref: writing-plugins-building-urls
title: Building URLs within plugins
breadcrumbs: ["Writing plugins"]
references: []
content:
Plugins that define their own custom user interface elements may need to link to other pages within Datasette. 
             This can be a bit tricky if the Datasette instance is using the  base_url  configuration setting to run behind a proxy, since that can cause Datasette's URLs to include an additional prefix. 
             The  datasette.urls  object provides internal methods for correctly generating URLs to different pages within Datasette, taking any  base_url  configuration into account. 
             This object is exposed in templates as the  urls  variable, which can be used like this: 
             Back to the <a href="{{ urls.instance() }}">Homepage</a> 
             See  datasette.urls  for full details on this object.


id: writing_plugins:writing-plugins-extra-hooks
page: writing_plugins
ref: writing-plugins-extra-hooks
title: Plugins that define new plugin hooks
breadcrumbs: ["Writing plugins"]
content:
Plugins can define new plugin hooks that other plugins can use to further extend their functionality. 
             datasette-graphql  is one example of a plugin that does this. It defines a new hook called  graphql_extra_fields ,  described here , which other plugins can use to define additional fields that should be included in the GraphQL schema. 
             To define additional hooks, add a file to the plugin called  datasette_your_plugin/hookspecs.py  with content that looks like this: 
             from pluggy import HookspecMarker

hookspec = HookspecMarker("datasette")


@hookspec
def name_of_your_hook_goes_here(datasette):
    "Description of your hook." 
             You should define your own hook name and arguments here, following the documentation for  Pluggy specifications . Make sure to pick a name that is unlikely to clash with hooks provided by any other plugins. 
             Then, to register your plugin hooks, add the following code to your  datasette_your_plugin/__init__.py  file: 
             from datasette.plugins import pm
from . import hookspecs

pm.add_hookspecs(hookspecs) 
             This will register your plugin hooks as part of the  datasette  plugin hook namespace. 
             Within your plugin code you can trigger the hook using this pattern: 
             from datasette.plugins import pm

for (
    plugin_return_value
) in pm.hook.name_of_your_hook_goes_here(
    datasette=datasette
):
    # Do something with plugin_return_value
    pass 
             Other plugins will then be able to register their own implementations of your hook using this syntax: 
             from datasette import hookimpl


@hookimpl
def name_of_your_hook_goes_here(datasette):
    return "Response from this plugin hook" 
             These plugin implementations can accept 0 or more of the named arguments that you defined in your hook specification.

references:
[{"href": "https://github.com/simonw/datasette-graphql", "label": "datasette-graphql"}, {"href": "https://github.com/simonw/datasette-graphql/blob/main/README.md#adding-custom-fields-with-plugins", "label": "described here"}, {"href": "https://pluggy.readthedocs.io/en/stable/#specs", "label": "Pluggy specifications"}]


id: sql_queries:sql
page: sql_queries
ref: sql
title: Running SQL queries
breadcrumbs: []
references: []
content:
Datasette treats SQLite database files as read-only and immutable. This means it is not possible to execute INSERT or UPDATE statements using Datasette, which allows us to expose SELECT statements to the outside world without needing to worry about SQL injection attacks. 
         The easiest way to execute custom SQL against Datasette is through the web UI. The database index page includes a SQL editor that lets you run any SELECT query you like. You can also construct queries using the filter interface on the tables page, then click "View and edit SQL" to open that query in the custom SQL editor. 
         Note that this interface is only available if the  execute-sql  permission is allowed. See  Controlling the ability to execute arbitrary SQL . 
         Any Datasette SQL query is reflected in the URL of the page, allowing you to bookmark them, share them with others and navigate through previous queries using your browser back button. 
         You can also retrieve the results of any query as JSON by adding  .json  to the base URL.


id: sql_queries:sql-parameters
page: sql_queries
ref: sql-parameters
title: Named parameters
breadcrumbs: ["Running SQL queries"]
references: [{"href": "https://github.com/simonw/datasette/issues/761", "label": "of exceptions"}]
content:
Datasette has special support for SQLite named parameters. Consider a SQL query like this: 
             select * from Street_Tree_List
where "PermitNotes" like :notes
and "qSpecies" = :species 
             If you execute this query using the custom query editor, Datasette will extract the two named parameters and use them to construct form fields for you to provide values. 
             You can also provide values for these fields by constructing a URL: 
             /mydatabase?sql=select...&species=44 
             SQLite string escaping rules will be applied to values passed using named parameters - they will be wrapped in quotes and their content will be correctly escaped. 
             Values from named parameters are treated as SQLite strings. If you need to perform numeric comparisons on them you should cast them to an integer or float first using  cast(:name as integer)  or  cast(:name as real) , for example: 
             select * from Street_Tree_List
where latitude > cast(:min_latitude as real)
and latitude < cast(:max_latitude as real) 
             Datasette disallows custom SQL queries containing the string PRAGMA (with a small number  of exceptions ) as SQLite pragma statements can be used to change database settings at runtime. If you need to include the string "pragma" in a query you can do so safely using a named parameter.


id: sql_queries:sql-views
page: sql_queries
ref: sql-views
title: Views
breadcrumbs: ["Running SQL queries"]
content:
If you want to bundle some pre-written SQL queries with your Datasette-hosted database you can do so in two ways. The first is to include SQL views in your database - Datasette will then list those views on your database index page. 
             The quickest way to create views is with the SQLite command-line interface: 
             sqlite3 sf-trees.db 
             SQLite version 3.19.3 2017-06-27 16:48:08
Enter ".help" for usage hints.
sqlite> CREATE VIEW demo_view AS select qSpecies from Street_Tree_List;
<CTRL+D> 
             You can also use the  sqlite-utils  tool to  create a view : 
             sqlite-utils create-view sf-trees.db demo_view "select qSpecies from Street_Tree_List"

references:
[{"href": "https://sqlite-utils.datasette.io/", "label": "sqlite-utils"}, {"href": "https://sqlite-utils.datasette.io/en/stable/cli.html#creating-views", "label": "create a view"}]


id: sql_queries:id1
page: sql_queries
ref: id1
title: Canned queries
breadcrumbs: ["Running SQL queries"]
references: []
content:
As an alternative to adding views to your database, you can define canned queries inside your  datasette.yaml  file. Here's an example: 
             [[[cog
from metadata_doc import config_example, config_example
config_example(cog, {
    "databases": {
       "sf-trees": {
           "queries": {
               "just_species": {
                   "sql": "select qSpecies from Street_Tree_List"
               }
           }
       }
    }
}) 
             ]]] 
             [[[end]]] 
             Then run Datasette like this: 
             datasette sf-trees.db -m metadata.json 
             Each canned query will be listed on the database index page, and will also get its own URL at: 
             /database-name/canned-query-name 
             For the above example, that URL would be: 
             /sf-trees/just_species 
             You can optionally include  "title"  and  "description"  keys to show a title and description on the canned query page. As with regular table metadata you can alternatively specify  "description_html"  to have your description rendered as HTML (rather than having HTML special characters escaped).


id: sql_queries:canned-queries-named-parameters
page: sql_queries
ref: canned-queries-named-parameters
title: Canned query parameters
breadcrumbs: ["Running SQL queries", "Canned queries"]
content:
Canned queries support named parameters, so if you include those in the SQL you will then be able to enter them using the form fields on the canned query page or by adding them to the URL. This means canned queries can be used to create custom JSON APIs based on a carefully designed SQL statement. 
                 Here's an example of a canned query with a named parameter: 
                 select neighborhood, facet_cities.name, state
from facetable
  join facet_cities on facetable.city_id = facet_cities.id
where neighborhood like '%' || :text || '%'
order by neighborhood; 
                 In the canned query configuration looks like this: 
                 [[[cog
config_example(cog, """
databases:
  fixtures:
    queries:
      neighborhood_search:
        title: Search neighborhoods
        sql: |-
          select neighborhood, facet_cities.name, state
          from facetable
            join facet_cities on facetable.city_id = facet_cities.id
          where neighborhood like '%' || :text || '%'
          order by neighborhood
""") 
                 ]]] 
                 [[[end]]] 
                 Note that we are using SQLite string concatenation here - the  ||  operator - to add wildcard  %  characters to the string provided by the user. 
                 You can try this canned query out here:
                     https://latest.datasette.io/fixtures/neighborhood_search?text=town 
                 In this example the  :text  named parameter is automatically extracted from the query using a regular expression. 
                 You can alternatively provide an explicit list of named parameters using the  "params"  key, like this: 
                 [[[cog
config_example(cog, """
databases:
  fixtures:
    queries:
      neighborhood_search:
        title: Search neighborhoods
        params:
        - text
        sql: |-
          select neighborhood, facet_cities.name, state
          from facetable
            join facet_cities on facetable.city_id = facet_cities.id
          where neighborhood like '%' || :text || '%'
          order by neighborhood
""") 
                 ]]] 
                 [[[end]]]

references:
[{"href": "https://latest.datasette.io/fixtures/neighborhood_search?text=town", "label": "https://latest.datasette.io/fixtures/neighborhood_search?text=town"}]


id: sql_queries:hide-sql
page: sql_queries
ref: hide-sql
title: hide_sql
breadcrumbs: ["Running SQL queries", "Canned queries", "Additional canned query options"]
references: []
content:
Canned queries default to displaying their SQL query at the top of the page. If the query is extremely long you may want to hide it by default, with a "show" link that can be used to make it visible. 
                     Add the  "hide_sql": true  option to hide the SQL query by default.


id: sql_queries:fragment
page: sql_queries
ref: fragment
title: fragment
breadcrumbs: ["Running SQL queries", "Canned queries", "Additional canned query options"]
content:
Some plugins, such as  datasette-vega , can be configured by including additional data in the fragment hash of the URL - the bit that comes after a  #  symbol. 
                     You can set a default fragment hash that will be included in the link to the canned query from the database index page using the  "fragment"  key. 
                     This example demonstrates both  fragment  and  hide_sql : 
                     [[[cog
config_example(cog, """
databases:
  fixtures:
    queries:
      neighborhood_search:
        fragment: fragment-goes-here
        hide_sql: true
        sql: |-
          select neighborhood, facet_cities.name, state
          from facetable join facet_cities on facetable.city_id = facet_cities.id
          where neighborhood like '%' || :text || '%' order by neighborhood;
""") 
                     ]]] 
                     [[[end]]] 
                     See here  for a demo of this in action.

references:
[{"href": "https://github.com/simonw/datasette-vega", "label": "datasette-vega"}, {"href": "https://latest.datasette.io/fixtures#queries", "label": "See here"}]


id: sql_queries:canned-queries-writable
page: sql_queries
ref: canned-queries-writable
title: Writable canned queries
breadcrumbs: ["Running SQL queries", "Canned queries"]
references: []
content:
Canned queries by default are read-only. You can use the  "write": true  key to indicate that a canned query can write to the database. 
                 See  Access to specific canned queries  for details on how to add permission checks to canned queries, using the  "allow"  key. 
                 [[[cog
config_example(cog, {
    "databases": {
        "mydatabase": {
            "queries": {
                "add_name": {
                    "sql": "INSERT INTO names (name) VALUES (:name)",
                    "write": True
                }
            }
        }
    }
}) 
                 ]]] 
                 [[[end]]] 
                 This configuration will create a page at  /mydatabase/add_name  displaying a form with a  name  field. Submitting that form will execute the configured  INSERT  query. 
                 You can customize how Datasette represents success and errors using the following optional properties: 
                 
                     
                         on_success_message  - the message shown when a query is successful 
                     
                     
                         on_success_message_sql  - alternative to  on_success_message : a SQL query that should be executed to generate the message 
                     
                     
                         on_success_redirect  - the path or URL the user is redirected to on success 
                     
                     
                         on_error_message  - the message shown when a query throws an error 
                     
                     
                         on_error_redirect  - the path or URL the user is redirected to on error 
                     
                 
                 For example: 
                 [[[cog
config_example(cog, {
    "databases": {
        "mydatabase": {
            "queries": {
                "add_name": {
                    "sql": "INSERT INTO names (name) VALUES (:name)",
                    "params": ["name"],
                    "write": True,
                    "on_success_message_sql": "select 'Name inserted: ' || :name",
                    "on_success_redirect": "/mydatabase/names",
                    "on_error_message": "Name insert failed",
                    "on_error_redirect": "/mydatabase",
                }
            }
        }
    }
}) 
                 ]]] 
                 [[[end]]] 
                 You can use  "params"  to explicitly list the named parameters that should be displayed as form fields - otherwise they will be automatically detected.  "params"  is not necessary in the above example, since without it  "name"  would be automatically detected from the query. 
                 You can pre-populate form fields when the page first loads using a query string, e.g.  /mydatabase/add_name?name=Prepopulated . The user will have to submit the form to execute the query. 
                 If you specify a query in  "on_success_message_sql" , that query will be executed after the main query. The first column of the first row return by that query will be displayed as a success message. Named parameters from the main query will be made available to the success message query as well.


id: sql_queries:canned-queries-magic-parameters
page: sql_queries
ref: canned-queries-magic-parameters
title: Magic parameters
breadcrumbs: ["Running SQL queries", "Canned queries"]
references: []
content:
Named parameters that start with an underscore are special: they can be used to automatically add values created by Datasette that are not contained in the incoming form fields or query string. 
                 These magic parameters are only supported for canned queries: to avoid security issues (such as queries that extract the user's private cookies) they are not available to SQL that is executed by the user as a custom SQL query. 
                 Available magic parameters are: 
                 
                     
                         _actor_*  - e.g.  _actor_id ,  _actor_name 
                         
                             Fields from the currently authenticated  Actors . 
                         
                     
                     
                         _header_*  - e.g.  _header_user_agent 
                         
                             Header from the incoming HTTP request. The key should be in lower case and with hyphens converted to underscores e.g.  _header_user_agent  or  _header_accept_language . 
                         
                     
                     
                         _cookie_*  - e.g.  _cookie_lang 
                         
                             The value of the incoming cookie of that name. 
                         
                     
                     
                         _now_epoch 
                         
                             The number of seconds since the Unix epoch. 
                         
                     
                     
                         _now_date_utc 
                         
                             The date in UTC, e.g.  2020-06-01 
                         
                     
                     
                         _now_datetime_utc 
                         
                             The ISO 8601 datetime in UTC, e.g.  2020-06-24T18:01:07Z 
                         
                     
                     
                         _random_chars_*  - e.g.  _random_chars_128 
                         
                             A random string of characters of the specified length. 
                         
                     
                 
                 Here's an example configuration that adds a message from the authenticated user, storing various pieces of additional metadata using magic parameters: 
                 [[[cog
config_example(cog, """
databases:
  mydatabase:
    queries:
      add_message:
        allow:
          id: "*"
        sql: |-
          INSERT INTO messages (
            user_id, message, datetime
          ) VALUES (
            :_actor_id, :message, :_now_datetime_utc
          )
        write: true
""") 
                 ]]] 
                 [[[end]]] 
                 The form presented at  /mydatabase/add_message  will have just a field for  message  - the other parameters will be populated by the magic parameter mechanism. 
                 Additional custom magic parameters can be added by plugins using the  register_magic_parameters(datasette)  hook.


id: sql_queries:canned-queries-json-api
page: sql_queries
ref: canned-queries-json-api
title: JSON API for writable canned queries
breadcrumbs: ["Running SQL queries", "Canned queries"]
references: []
content:
Writable canned queries can also be accessed using a JSON API. You can POST data to them using JSON, and you can request that their response is returned to you as JSON. 
                 To submit JSON to a writable canned query, encode key/value parameters as a JSON document: 
                 POST /mydatabase/add_message

{"message": "Message goes here"} 
                 You can also continue to submit data using regular form encoding, like so: 
                 POST /mydatabase/add_message

message=Message+goes+here 
                 There are three options for specifying that you would like the response to your request to return JSON data, as opposed to an HTTP redirect to another page. 
                 
                     
                         Set an  Accept: application/json  header on your request 
                     
                     
                         Include  ?_json=1  in the URL that you POST to 
                     
                     
                         Include  "_json": 1  in your JSON body, or  &_json=1  in your form encoded body 
                     
                 
                 The JSON response will look like this: 
                 {
    "ok": true,
    "message": "Query executed, 1 row affected",
    "redirect": "/data/add_name"
} 
                 The  "message"  and  "redirect"  values here will take into account  on_success_message ,  on_success_message_sql ,   on_success_redirect ,  on_error_message  and  on_error_redirect , if they have been set.


id: sql_queries:id2
page: sql_queries
ref: id2
title: Pagination
breadcrumbs: ["Running SQL queries"]
references: []
content:
Datasette's default table pagination is designed to be extremely efficient. SQL OFFSET/LIMIT pagination can have a significant performance penalty once you get into multiple thousands of rows, as each page still requires the database to scan through every preceding row to find the correct offset. 
             When paginating through tables, Datasette instead orders the rows in the table by their primary key and performs a WHERE clause against the last seen primary key for the previous page. For example: 
             select rowid, * from Tree_List where rowid > 200 order by rowid limit 101 
             This represents page three for this particular table, with a page size of 100. 
             Note that we request 101 items in the limit clause rather than 100. This allows us to detect if we are on the last page of the results: if the query returns less than 101 rows we know we have reached the end of the pagination set. Datasette will only return the first 100 rows - the 101st is used purely to detect if there should be another page. 
             Since the where clause acts against the index on the primary key, the query is extremely fast even for records that are a long way into the overall pagination set.


id: sql_queries:id3
page: sql_queries
ref: id3
title: Cross-database queries
breadcrumbs: ["Running SQL queries"]
content:
SQLite has the ability to run queries that join across multiple databases. Up to ten databases can be attached to a single SQLite connection and queried together. 
             Datasette can execute joins across multiple databases if it is started with the  --crossdb  option: 
             datasette fixtures.db extra_database.db --crossdb 
             If it is started in this way, the  /_memory  page can be used to execute queries that join across multiple databases. 
             References to tables in attached databases should be preceded by the database name and a period. 
             For example, this query will show a list of tables across both of the above databases: 
             select
  'fixtures' as database, *
from
  [fixtures].sqlite_master
union
select
  'extra_database' as database, *
from
  [extra_database].sqlite_master 
             Try that out here .

references:
[{"href": "https://latest.datasette.io/_memory?sql=select%0D%0A++%27fixtures%27+as+database%2C+*%0D%0Afrom%0D%0A++%5Bfixtures%5D.sqlite_master%0D%0Aunion%0D%0Aselect%0D%0A++%27extra_database%27+as+database%2C+*%0D%0Afrom%0D%0A++%5Bextra_database%5D.sqlite_master", "label": "Try that out here"}]


id: cli-reference:id1
page: cli-reference
ref: id1
title: CLI reference
breadcrumbs: []
references: []
content:
The  datasette  CLI tool provides a number of commands. 
         Running  datasette  without specifying a command runs the default command,  datasette serve .  See  datasette serve  for the full list of options for that command. 
         [[[cog
from datasette import cli
from click.testing import CliRunner
import textwrap
def help(args):
    title = "datasette " + " ".join(args)
    cog.out("\n::\n\n")
    result = CliRunner().invoke(cli.cli, args)
    output = result.output.replace("Usage: cli ", "Usage: datasette ")
    cog.out(textwrap.indent(output, '    '))
    cog.out("\n\n") 
         ]]] 
         [[[end]]]


id: cli-reference:cli-help-help
page: cli-reference
ref: cli-help-help
title: datasette --help
breadcrumbs: ["CLI reference"]
references: []
content:
Running  datasette --help  shows a list of all of the available commands. 
             [[[cog
help(["--help"]) 
             ]]] 
             Usage: datasette [OPTIONS] COMMAND [ARGS]...

  Datasette is an open source multi-tool for exploring and publishing data

  About Datasette: https://datasette.io/
  Full documentation: https://docs.datasette.io/

Options:
  --version  Show the version and exit.
  --help     Show this message and exit.

Commands:
  serve*        Serve up specified SQLite database files with a web UI
  create-token  Create a signed API token for the specified actor ID
  inspect       Generate JSON summary of provided database files
  install       Install plugins and packages from PyPI into the same...
  package       Package SQLite files into a Datasette Docker container
  plugins       List currently installed plugins
  publish       Publish specified SQLite database files to the internet...
  uninstall     Uninstall plugins and Python packages from the Datasette... 
             [[[end]]] 
             Additional commands added by plugins that use the  register_commands(cli)  hook will be listed here as well.


id: cli-reference:cli-help-serve-help
page: cli-reference
ref: cli-help-serve-help
title: datasette serve
breadcrumbs: ["CLI reference"]
references: []
content:
This command starts the Datasette web application running on your machine: 
             datasette serve mydatabase.db 
             Or since this is the default command you can run this instead: 
             datasette mydatabase.db 
             Once started you can access it at  http://localhost:8001 
             [[[cog
help(["serve", "--help"]) 
             ]]] 
             Usage: datasette serve [OPTIONS] [FILES]...

  Serve up specified SQLite database files with a web UI

Options:
  -i, --immutable PATH            Database files to open in immutable mode
  -h, --host TEXT                 Host for server. Defaults to 127.0.0.1 which
                                  means only connections from the local machine
                                  will be allowed. Use 0.0.0.0 to listen to all
                                  IPs and allow access from other machines.
  -p, --port INTEGER RANGE        Port for server, defaults to 8001. Use -p 0 to
                                  automatically assign an available port.
                                  [0<=x<=65535]
  --uds TEXT                      Bind to a Unix domain socket
  --reload                        Automatically reload if code or metadata
                                  change detected - useful for development
  --cors                          Enable CORS by serving Access-Control-Allow-
                                  Origin: *
  --load-extension PATH:ENTRYPOINT?
                                  Path to a SQLite extension to load, and
                                  optional entrypoint
  --inspect-file TEXT             Path to JSON file created using "datasette
                                  inspect"
  -m, --metadata FILENAME         Path to JSON/YAML file containing
                                  license/source metadata
  --template-dir DIRECTORY        Path to directory containing custom templates
  --plugins-dir DIRECTORY         Path to directory containing custom plugins
  --static MOUNT:DIRECTORY        Serve static files from this directory at
                                  /MOUNT/...
  --memory                        Make /_memory database available
  -c, --config FILENAME           Path to JSON/YAML Datasette configuration file
  -s, --setting SETTING...        nested.key, value setting to use in Datasette
                                  configuration
  --secret TEXT                   Secret used for signing secure values, such as
                                  signed cookies
  --root                          Output URL that sets a cookie authenticating
                                  the root user
  --get TEXT                      Run an HTTP GET request against this path,
                                  print results and exit
  --token TEXT                    API token to send with --get requests
  --actor TEXT                    Actor to use for --get requests (JSON string)
  --version-note TEXT             Additional note to show on /-/versions
  --help-settings                 Show available settings
  --pdb                           Launch debugger on any errors
  -o, --open                      Open Datasette in your web browser
  --create                        Create database files if they do not exist
  --crossdb                       Enable cross-database joins using the /_memory
                                  database
  --nolock                        Ignore locking, open locked files in read-only
                                  mode
  --ssl-keyfile TEXT              SSL key file
  --ssl-certfile TEXT             SSL certificate file
  --internal PATH                 Path to a persistent Datasette internal SQLite
                                  database
  --help                          Show this message and exit. 
             [[[end]]]


id: cli-reference:cli-datasette-get
page: cli-reference
ref: cli-datasette-get
title: datasette --get
breadcrumbs: ["CLI reference", "datasette serve"]
references: []
content:
The  --get  option to  datasette serve  (or just  datasette ) specifies the path to a page within Datasette and causes Datasette to output the content from that path without starting the web server. 
                 This means that all of Datasette's functionality can be accessed directly from the command-line. 
                 For example: 
                 datasette --get '/-/versions.json' | jq . 
                 {
  "python": {
    "version": "3.8.5",
    "full": "3.8.5 (default, Jul 21 2020, 10:48:26) \n[Clang 11.0.3 (clang-1103.0.32.62)]"
  },
  "datasette": {
    "version": "0.46+15.g222a84a.dirty"
  },
  "asgi": "3.0",
  "uvicorn": "0.11.8",
  "sqlite": {
    "version": "3.32.3",
    "fts_versions": [
      "FTS5",
      "FTS4",
      "FTS3"
    ],
    "extensions": {
      "json1": null
    },
    "compile_options": [
      "COMPILER=clang-11.0.3",
      "ENABLE_COLUMN_METADATA",
      "ENABLE_FTS3",
      "ENABLE_FTS3_PARENTHESIS",
      "ENABLE_FTS4",
      "ENABLE_FTS5",
      "ENABLE_GEOPOLY",
      "ENABLE_JSON1",
      "ENABLE_PREUPDATE_HOOK",
      "ENABLE_RTREE",
      "ENABLE_SESSION",
      "MAX_VARIABLE_NUMBER=250000",
      "THREADSAFE=1"
    ]
  }
} 
                 You can use the  --token TOKEN  option to send an  API token  with the simulated request. 
                 Or you can make a request as a specific actor by passing a JSON representation of that actor to  --actor : 
                 datasette --memory --actor '{"id": "root"}' --get '/-/actor.json' 
                 The exit code of  datasette --get  will be 0 if the request succeeds and 1 if the request produced an HTTP status code other than 200 - e.g. a 404 or 500 error. 
                 This lets you use  datasette --get /  to run tests against a Datasette application in a continuous integration environment such as GitHub Actions.


id: cli-reference:cli-help-serve-help-settings
page: cli-reference
ref: cli-help-serve-help-settings
title: datasette serve --help-settings
breadcrumbs: ["CLI reference", "datasette serve"]
references: []
content:
This command outputs all of the available Datasette  settings . 
                 These can be passed to  datasette serve  using  datasette serve --setting name value . 
                 [[[cog
help(["--help-settings"]) 
                 ]]] 
                 Settings:
  default_page_size            Default page size for the table view
                               (default=100)
  max_returned_rows            Maximum rows that can be returned from a table or
                               custom query (default=1000)
  max_insert_rows              Maximum rows that can be inserted at a time using
                               the bulk insert API (default=100)
  num_sql_threads              Number of threads in the thread pool for
                               executing SQLite queries (default=3)
  sql_time_limit_ms            Time limit for a SQL query in milliseconds
                               (default=1000)
  default_facet_size           Number of values to return for requested facets
                               (default=30)
  facet_time_limit_ms          Time limit for calculating a requested facet
                               (default=200)
  facet_suggest_time_limit_ms  Time limit for calculating a suggested facet
                               (default=50)
  allow_facet                  Allow users to specify columns to facet using
                               ?_facet= parameter (default=True)
  allow_download               Allow users to download the original SQLite
                               database files (default=True)
  allow_signed_tokens          Allow users to create and use signed API tokens
                               (default=True)
  default_allow_sql            Allow anyone to run arbitrary SQL queries
                               (default=True)
  max_signed_tokens_ttl        Maximum allowed expiry time for signed API tokens
                               (default=0)
  suggest_facets               Calculate and display suggested facets
                               (default=True)
  default_cache_ttl            Default HTTP cache TTL (used in Cache-Control:
                               max-age= header) (default=5)
  cache_size_kb                SQLite cache size in KB (0 == use SQLite default)
                               (default=0)
  allow_csv_stream             Allow .csv?_stream=1 to download all rows
                               (ignoring max_returned_rows) (default=True)
  max_csv_mb                   Maximum size allowed for CSV export in MB - set 0
                               to disable this limit (default=100)
  truncate_cells_html          Truncate cells longer than this in HTML table
                               view - set 0 to disable (default=2048)
  force_https_urls             Force URLs in API output to always use https://
                               protocol (default=False)
  template_debug               Allow display of template debug information with
                               ?_context=1 (default=False)
  trace_debug                  Allow display of SQL trace debug information with
                               ?_trace=1 (default=False)
  base_url                     Datasette URLs should use this base path
                               (default=/) 
                 [[[end]]]


id: cli-reference:cli-help-plugins-help
page: cli-reference
ref: cli-help-plugins-help
title: datasette plugins
breadcrumbs: ["CLI reference"]
references: []
content:
Output JSON showing all currently installed plugins, their versions, whether they include static files or templates and which  Plugin hooks  they use. 
             [[[cog
help(["plugins", "--help"]) 
             ]]] 
             Usage: datasette plugins [OPTIONS]

  List currently installed plugins

Options:
  --all                    Include built-in default plugins
  --requirements           Output requirements.txt of installed plugins
  --plugins-dir DIRECTORY  Path to directory containing custom plugins
  --help                   Show this message and exit. 
             [[[end]]] 
             Example output: 
             [
    {
        "name": "datasette-geojson",
        "static": false,
        "templates": false,
        "version": "0.3.1",
        "hooks": [
            "register_output_renderer"
        ]
    },
    {
        "name": "datasette-geojson-map",
        "static": true,
        "templates": false,
        "version": "0.4.0",
        "hooks": [
            "extra_body_script",
            "extra_css_urls",
            "extra_js_urls"
        ]
    },
    {
        "name": "datasette-leaflet",
        "static": true,
        "templates": false,
        "version": "0.2.2",
        "hooks": [
            "extra_body_script",
            "extra_template_vars"
        ]
    }
]


id: cli-reference:cli-help-install-help
page: cli-reference
ref: cli-help-install-help
title: datasette install
breadcrumbs: ["CLI reference"]
references: [{"href": "https://datasette.io/plugins/datasette-cluster-map", "label": "datasette-cluster-map"}]
content:
Install new Datasette plugins. This command works like  pip install  but ensures that your plugins will be installed into the same environment as Datasette. 
             This command: 
             datasette install datasette-cluster-map 
             Would install the  datasette-cluster-map  plugin. 
             [[[cog
help(["install", "--help"]) 
             ]]] 
             Usage: datasette install [OPTIONS] [PACKAGES]...

  Install plugins and packages from PyPI into the same environment as Datasette

Options:
  -U, --upgrade           Upgrade packages to latest version
  -r, --requirement PATH  Install from requirements file
  -e, --editable TEXT     Install a project in editable mode from this path
  --help                  Show this message and exit. 
             [[[end]]]


id: cli-reference:cli-help-uninstall-help
page: cli-reference
ref: cli-help-uninstall-help
title: datasette uninstall
breadcrumbs: ["CLI reference"]
references: []
content:
Uninstall one or more plugins. 
             [[[cog
help(["uninstall", "--help"]) 
             ]]] 
             Usage: datasette uninstall [OPTIONS] PACKAGES...

  Uninstall plugins and Python packages from the Datasette environment

Options:
  -y, --yes  Don't ask for confirmation
  --help     Show this message and exit. 
             [[[end]]]


id: cli-reference:cli-help-publish-help
page: cli-reference
ref: cli-help-publish-help
title: datasette publish
breadcrumbs: ["CLI reference"]
references: []
content:
Shows a list of available deployment targets for  publishing data  with Datasette. 
             Additional deployment targets can be added by plugins that use the  publish_subcommand(publish)  hook. 
             [[[cog
help(["publish", "--help"]) 
             ]]] 
             Usage: datasette publish [OPTIONS] COMMAND [ARGS]...

  Publish specified SQLite database files to the internet along with a
  Datasette-powered interface and API

Options:
  --help  Show this message and exit.

Commands:
  cloudrun  Publish databases to Datasette running on Cloud Run
  heroku    Publish databases to Datasette running on Heroku 
             [[[end]]]


id: cli-reference:cli-help-publish-cloudrun-help
page: cli-reference
ref: cli-help-publish-cloudrun-help
title: datasette publish cloudrun
breadcrumbs: ["CLI reference"]
references: []
content:
See  Publishing to Google Cloud Run . 
             [[[cog
help(["publish", "cloudrun", "--help"]) 
             ]]] 
             Usage: datasette publish cloudrun [OPTIONS] [FILES]...

  Publish databases to Datasette running on Cloud Run

Options:
  -m, --metadata FILENAME         Path to JSON/YAML file containing metadata to
                                  publish
  --extra-options TEXT            Extra options to pass to datasette serve
  --branch TEXT                   Install datasette from a GitHub branch e.g.
                                  main
  --template-dir DIRECTORY        Path to directory containing custom templates
  --plugins-dir DIRECTORY         Path to directory containing custom plugins
  --static MOUNT:DIRECTORY        Serve static files from this directory at
                                  /MOUNT/...
  --install TEXT                  Additional packages (e.g. plugins) to install
  --plugin-secret <TEXT TEXT TEXT>...
                                  Secrets to pass to plugins, e.g. --plugin-
                                  secret datasette-auth-github client_id xxx
  --version-note TEXT             Additional note to show on /-/versions
  --secret TEXT                   Secret used for signing secure values, such as
                                  signed cookies
  --title TEXT                    Title for metadata
  --license TEXT                  License label for metadata
  --license_url TEXT              License URL for metadata
  --source TEXT                   Source label for metadata
  --source_url TEXT               Source URL for metadata
  --about TEXT                    About label for metadata
  --about_url TEXT                About URL for metadata
  -n, --name TEXT                 Application name to use when building
  --service TEXT                  Cloud Run service to deploy (or over-write)
  --spatialite                    Enable SpatialLite extension
  --show-files                    Output the generated Dockerfile and
                                  metadata.json
  --memory TEXT                   Memory to allocate in Cloud Run, e.g. 1Gi
  --cpu [1|2|4]                   Number of vCPUs to allocate in Cloud Run
  --timeout INTEGER               Build timeout in seconds
  --apt-get-install TEXT          Additional packages to apt-get install
  --max-instances INTEGER         Maximum Cloud Run instances
  --min-instances INTEGER         Minimum Cloud Run instances
  --help                          Show this message and exit. 
             [[[end]]]


id: cli-reference:cli-help-publish-heroku-help
page: cli-reference
ref: cli-help-publish-heroku-help
title: datasette publish heroku
breadcrumbs: ["CLI reference"]
references: []
content:
See  Publishing to Heroku . 
             [[[cog
help(["publish", "heroku", "--help"]) 
             ]]] 
             Usage: datasette publish heroku [OPTIONS] [FILES]...

  Publish databases to Datasette running on Heroku

Options:
  -m, --metadata FILENAME         Path to JSON/YAML file containing metadata to
                                  publish
  --extra-options TEXT            Extra options to pass to datasette serve
  --branch TEXT                   Install datasette from a GitHub branch e.g.
                                  main
  --template-dir DIRECTORY        Path to directory containing custom templates
  --plugins-dir DIRECTORY         Path to directory containing custom plugins
  --static MOUNT:DIRECTORY        Serve static files from this directory at
                                  /MOUNT/...
  --install TEXT                  Additional packages (e.g. plugins) to install
  --plugin-secret <TEXT TEXT TEXT>...
                                  Secrets to pass to plugins, e.g. --plugin-
                                  secret datasette-auth-github client_id xxx
  --version-note TEXT             Additional note to show on /-/versions
  --secret TEXT                   Secret used for signing secure values, such as
                                  signed cookies
  --title TEXT                    Title for metadata
  --license TEXT                  License label for metadata
  --license_url TEXT              License URL for metadata
  --source TEXT                   Source label for metadata
  --source_url TEXT               Source URL for metadata
  --about TEXT                    About label for metadata
  --about_url TEXT                About URL for metadata
  -n, --name TEXT                 Application name to use when deploying
  --tar TEXT                      --tar option to pass to Heroku, e.g.
                                  --tar=/usr/local/bin/gtar
  --generate-dir DIRECTORY        Output generated application files and stop
                                  without deploying
  --help                          Show this message and exit. 
             [[[end]]]


id: cli-reference:cli-help-package-help
page: cli-reference
ref: cli-help-package-help
title: datasette package
breadcrumbs: ["CLI reference"]
references: []
content:
Package SQLite files into a Datasette Docker container, see  datasette package . 
             [[[cog
help(["package", "--help"]) 
             ]]] 
             Usage: datasette package [OPTIONS] FILES...

  Package SQLite files into a Datasette Docker container

Options:
  -t, --tag TEXT            Name for the resulting Docker container, can
                            optionally use name:tag format
  -m, --metadata FILENAME   Path to JSON/YAML file containing metadata to
                            publish
  --extra-options TEXT      Extra options to pass to datasette serve
  --branch TEXT             Install datasette from a GitHub branch e.g. main
  --template-dir DIRECTORY  Path to directory containing custom templates
  --plugins-dir DIRECTORY   Path to directory containing custom plugins
  --static MOUNT:DIRECTORY  Serve static files from this directory at /MOUNT/...
  --install TEXT            Additional packages (e.g. plugins) to install
  --spatialite              Enable SpatialLite extension
  --version-note TEXT       Additional note to show on /-/versions
  --secret TEXT             Secret used for signing secure values, such as
                            signed cookies
  -p, --port INTEGER RANGE  Port to run the server on, defaults to 8001
                            [1<=x<=65535]
  --title TEXT              Title for metadata
  --license TEXT            License label for metadata
  --license_url TEXT        License URL for metadata
  --source TEXT             Source label for metadata
  --source_url TEXT         Source URL for metadata
  --about TEXT              About label for metadata
  --about_url TEXT          About URL for metadata
  --help                    Show this message and exit. 
             [[[end]]]


id: cli-reference:cli-help-inspect-help
page: cli-reference
ref: cli-help-inspect-help
title: datasette inspect
breadcrumbs: ["CLI reference"]
references: []
content:
Outputs JSON representing introspected data about one or more SQLite database files. 
             If you are opening an immutable database, you can pass this file to the  --inspect-data  option to improve Datasette's performance by allowing it to skip running row counts against the database when it first starts running: 
             datasette inspect mydatabase.db > inspect-data.json
datasette serve -i mydatabase.db --inspect-file inspect-data.json 
             This performance optimization is used automatically by some of the  datasette publish  commands. You are unlikely to need to apply this optimization manually. 
             [[[cog
help(["inspect", "--help"]) 
             ]]] 
             Usage: datasette inspect [OPTIONS] [FILES]...

  Generate JSON summary of provided database files

  This can then be passed to "datasette --inspect-file" to speed up count
  operations against immutable database files.

Options:
  --inspect-file TEXT
  --load-extension PATH:ENTRYPOINT?
                                  Path to a SQLite extension to load, and
                                  optional entrypoint
  --help                          Show this message and exit. 
             [[[end]]]


id: cli-reference:cli-help-create-token-help
page: cli-reference
ref: cli-help-create-token-help
title: datasette create-token
breadcrumbs: ["CLI reference"]
references: []
content:
Create a signed API token, see  datasette create-token . 
             [[[cog
help(["create-token", "--help"]) 
             ]]] 
             Usage: datasette create-token [OPTIONS] ID

  Create a signed API token for the specified actor ID

  Example:

      datasette create-token root --secret mysecret

  To allow only "view-database-download" for all databases:

      datasette create-token root --secret mysecret \
          --all view-database-download

  To allow "create-table" against a specific database:

      datasette create-token root --secret mysecret \
          --database mydb create-table

  To allow "insert-row" against a specific table:

      datasette create-token root --secret myscret \
          --resource mydb mytable insert-row

  Restricted actions can be specified multiple times using multiple --all,
  --database, and --resource options.

  Add --debug to see a decoded version of the token.

Options:
  --secret TEXT                   Secret used for signing the API tokens
                                  [required]
  -e, --expires-after INTEGER     Token should expire after this many seconds
  -a, --all ACTION                Restrict token to this action
  -d, --database DB ACTION        Restrict token to this action on this database
  -r, --resource DB RESOURCE ACTION
                                  Restrict token to this action on this database
                                  resource (a table, SQL view or named query)
  --debug                         Show decoded token
  --plugins-dir DIRECTORY         Path to directory containing custom plugins
  --help                          Show this message and exit. 
             [[[end]]]


id: plugin_hooks:id1
page: plugin_hooks
ref: id1
title: Plugin hooks
breadcrumbs: []
references: [{"href": "https://pluggy.readthedocs.io/", "label": "pluggy"}]
content:
Datasette  plugins  use  plugin hooks  to customize Datasette's behavior. These hooks are powered by the  pluggy  plugin system. 
         Each plugin can implement one or more hooks using the  @hookimpl  decorator against a function named that matches one of the hooks documented on this page. 
         When you implement a plugin hook you can accept any or all of the parameters that are documented as being passed to that hook. 
         For example, you can implement the  render_cell  plugin hook like this even though the full documented hook signature is  render_cell(row, value, column, table, database, datasette) : 
         @hookimpl
def render_cell(value, column):
    if column == "stars":
        return "*" * int(value) 
         
             List of plugin hooks 
             
                 
                     prepare_connection(conn, database, datasette) 
                 
                 
                     prepare_jinja2_environment(env, datasette) 
                 
                 
                     Page extras 
                     
                         
                             extra_template_vars(template, database, table, columns, view_name, request, datasette) 
                         
                         
                             extra_css_urls(template, database, table, columns, view_name, request, datasette) 
                         
                         
                             extra_js_urls(template, database, table, columns, view_name, request, datasette) 
                         
                         
                             extra_body_script(template, database, table, columns, view_name, request, datasette) 
                         
                     
                 
                 
                     publish_subcommand(publish) 
                 
                 
                     render_cell(row, value, column, table, database, datasette, request) 
                 
                 
                     register_output_renderer(datasette) 
                 
                 
                     register_routes(datasette) 
                 
                 
                     register_commands(cli) 
                 
                 
                     register_facet_classes() 
                 
                 
                     register_permissions(datasette) 
                 
                 
                     asgi_wrapper(datasette) 
                 
                 
                     startup(datasette) 
                 
                 
                     canned_queries(datasette, database, actor) 
                 
                 
                     actor_from_request(datasette, request) 
                 
                 
                     actors_from_ids(datasette, actor_ids) 
                 
                 
                     jinja2_environment_from_request(datasette, request, env) 
                 
                 
                     filters_from_request(request, database, table, datasette) 
                 
                 
                     permission_allowed(datasette, actor, action, resource) 
                 
                 
                     register_magic_parameters(datasette) 
                 
                 
                     forbidden(datasette, request, message) 
                 
                 
                     handle_exception(datasette, request, exception) 
                 
                 
                     skip_csrf(datasette, scope) 
                 
                 
                     get_metadata(datasette, key, database, table) 
                 
                 
                     menu_links(datasette, actor, request) 
                 
                 
                     Action hooks 
                     
                         
                             table_actions(datasette, actor, database, table, request) 
                         
                         
                             view_actions(datasette, actor, database, view, request) 
                         
                         
                             query_actions(datasette, actor, database, query_name, request, sql, params) 
                         
                         
                             row_actions(datasette, actor, request, database, table, row) 
                         
                         
                             database_actions(datasette, actor, database, request) 
                         
                         
                             homepage_actions(datasette, actor, request) 
                         
                     
                 
                 
                     Template slots 
                     
                         
                             top_homepage(datasette, request) 
                         
                         
                             top_database(datasette, request, database) 
                         
                         
                             top_table(datasette, request, database, table) 
                         
                         
                             top_row(datasette, request, database, table, row) 
                         
                         
                             top_query(datasette, request, database, sql) 
                         
                         
                             top_canned_query(datasette, request, database, query_name) 
                         
                     
                 
                 
                     Event tracking 
                     
                         
                             track_event(datasette, event) 
                         
                         
                             register_events(datasette)


id: plugin_hooks:plugin-hook-prepare-connection
page: plugin_hooks
ref: plugin-hook-prepare-connection
title: prepare_connection(conn, database, datasette)
breadcrumbs: ["Plugin hooks"]
content:
conn  - sqlite3 connection object 
                     
                         The connection that is being opened 
                     
                 
                 
                     database  - string 
                     
                         The name of the database 
                     
                 
                 
                     datasette  -  Datasette class 
                     
                         You can use this to access plugin configuration options via  datasette.plugin_config(your_plugin_name) 
                     
                 
             
             This hook is called when a new SQLite database connection is created. You can
                use it to  register custom SQL functions ,
                aggregates and collations. For example: 
             from datasette import hookimpl
import random


@hookimpl
def prepare_connection(conn):
    conn.create_function(
        "random_integer", 2, random.randint
    ) 
             This registers a SQL function called  random_integer  which takes two
                arguments and can be called like this: 
             select random_integer(1, 10); 
             Examples:  datasette-jellyfish ,  datasette-jq ,  datasette-haversine ,  datasette-rure

references:
[{"href": "https://docs.python.org/2/library/sqlite3.html#sqlite3.Connection.create_function", "label": "register custom SQL functions"}, {"href": "https://datasette.io/plugins/datasette-jellyfish", "label": "datasette-jellyfish"}, {"href": "https://datasette.io/plugins/datasette-jq", "label": "datasette-jq"}, {"href": "https://datasette.io/plugins/datasette-haversine", "label": "datasette-haversine"}, {"href": "https://datasette.io/plugins/datasette-rure", "label": "datasette-rure"}]


id: plugin_hooks:plugin-hook-prepare-jinja2-environment
page: plugin_hooks
ref: plugin-hook-prepare-jinja2-environment
title: prepare_jinja2_environment(env, datasette)
breadcrumbs: ["Plugin hooks"]
content:
env  - jinja2 Environment 
                     
                         The template environment that is being prepared 
                     
                 
                 
                     datasette  -  Datasette class 
                     
                         You can use this to access plugin configuration options via  datasette.plugin_config(your_plugin_name) 
                     
                 
             
             This hook is called with the Jinja2 environment that is used to evaluate
                Datasette HTML templates. You can use it to do things like  register custom
                    template filters , for
                example: 
             from datasette import hookimpl


@hookimpl
def prepare_jinja2_environment(env):
    env.filters["uppercase"] = lambda u: u.upper() 
             You can now use this filter in your custom templates like so: 
             Table name: {{ table|uppercase }} 
             This function can return an awaitable function if it needs to run any async code. 
             Examples:  datasette-edit-templates

references:
[{"href": "http://jinja.pocoo.org/docs/2.10/api/#custom-filters", "label": "register custom\n                    template filters"}, {"href": "https://datasette.io/plugins/datasette-edit-templates", "label": "datasette-edit-templates"}]


id: plugin_hooks:plugin-page-extras
page: plugin_hooks
ref: plugin-page-extras
title: Page extras
breadcrumbs: ["Plugin hooks"]
references: []
content:
These plugin hooks can be used to affect the way HTML pages for different Datasette interfaces are rendered.


id: plugin_hooks:plugin-hook-extra-template-vars
page: plugin_hooks
ref: plugin-hook-extra-template-vars
title: extra_template_vars(template, database, table, columns, view_name, request, datasette)
breadcrumbs: ["Plugin hooks", "Page extras"]
content:
Extra template variables that should be made available in the rendered template context. 
                 
                     
                         template  - string 
                         
                             The template that is being rendered, e.g.  database.html 
                         
                     
                     
                         database  - string or None 
                         
                             The name of the database, or  None  if the page does not correspond to a database (e.g. the root page) 
                         
                     
                     
                         table  - string or None 
                         
                             The name of the table, or  None  if the page does not correct to a table 
                         
                     
                     
                         columns  - list of strings or None 
                         
                             The names of the database columns that will be displayed on this page.  None  if the page does not contain a table. 
                         
                     
                     
                         view_name  - string 
                         
                             The name of the view being displayed. ( index ,  database ,  table , and  row  are the most important ones.) 
                         
                     
                     
                         request  -  Request object  or None 
                         
                             The current HTTP request. This can be  None  if the request object is not available. 
                         
                     
                     
                         datasette  -  Datasette class 
                         
                             You can use this to access plugin configuration options via  datasette.plugin_config(your_plugin_name) 
                         
                     
                 
                 This hook can return one of three different types: 
                 
                     
                         Dictionary 
                         
                             If you return a dictionary its keys and values will be merged into the template context. 
                         
                     
                     
                         Function that returns a dictionary 
                         
                             If you return a function it will be executed. If it returns a dictionary those values will will be merged into the template context. 
                         
                     
                     
                         Function that returns an awaitable function that returns a dictionary 
                         
                             You can also return a function which returns an awaitable function which returns a dictionary. 
                         
                     
                 
                 Datasette runs Jinja2 in  async mode , which means you can add awaitable functions to the template scope and they will be automatically awaited when they are rendered by the template. 
                 Here's an example plugin that adds a  "user_agent"  variable to the template context containing the current request's User-Agent header: 
                 @hookimpl
def extra_template_vars(request):
    return {"user_agent": request.headers.get("user-agent")} 
                 This example returns an awaitable function which adds a list of  hidden_table_names  to the context: 
                 @hookimpl
def extra_template_vars(datasette, database):
    async def hidden_table_names():
        if database:
            db = datasette.databases[database]
            return {
                "hidden_table_names": await db.hidden_table_names()
            }
        else:
            return {}

    return hidden_table_names 
                 And here's an example which adds a  sql_first(sql_query)  function which executes a SQL statement and returns the first column of the first row of results: 
                 @hookimpl
def extra_template_vars(datasette, database):
    async def sql_first(sql, dbname=None):
        dbname = (
            dbname
            or database
            or next(iter(datasette.databases.keys()))
        )
        result = await datasette.execute(dbname, sql)
        return result.rows[0][0]

    return {"sql_first": sql_first} 
                 You can then use the new function in a template like so: 
                 SQLite version: {{ sql_first("select sqlite_version()") }} 
                 Examples:  datasette-search-all ,  datasette-template-sql

references:
[{"href": "https://jinja.palletsprojects.com/en/2.10.x/api/#async-support", "label": "async mode"}, {"href": "https://datasette.io/plugins/datasette-search-all", "label": "datasette-search-all"}, {"href": "https://datasette.io/plugins/datasette-template-sql", "label": "datasette-template-sql"}]


id: plugin_hooks:plugin-hook-extra-css-urls
page: plugin_hooks
ref: plugin-hook-extra-css-urls
title: extra_css_urls(template, database, table, columns, view_name, request, datasette)
breadcrumbs: ["Plugin hooks", "Page extras"]
content:
This takes the same arguments as  extra_template_vars(...) 
                 Return a list of extra CSS URLs that should be included on the page. These can
                    take advantage of the CSS class hooks described in  Custom pages and templates . 
                 This can be a list of URLs: 
                 from datasette import hookimpl


@hookimpl
def extra_css_urls():
    return [
        "https://stackpath.bootstrapcdn.com/bootstrap/4.1.0/css/bootstrap.min.css"
    ] 
                 Or a list of dictionaries defining both a URL and an
                     SRI hash : 
                 @hookimpl
def extra_css_urls():
    return [
        {
            "url": "https://stackpath.bootstrapcdn.com/bootstrap/4.1.0/css/bootstrap.min.css",
            "sri": "sha384-9gVQ4dYFwwWSjIDZnLEWnxCjeSWFphJiwGPXr1jddIhOegiu1FwO5qRGvFXOdJZ4",
        }
    ] 
                 This function can also return an awaitable function, useful if it needs to run any async code: 
                 @hookimpl
def extra_css_urls(datasette):
    async def inner():
        db = datasette.get_database()
        results = await db.execute(
            "select url from css_files"
        )
        return [r[0] for r in results]

    return inner 
                 Examples:  datasette-cluster-map ,  datasette-vega

references:
[{"href": "https://www.srihash.org/", "label": "SRI hash"}, {"href": "https://datasette.io/plugins/datasette-cluster-map", "label": "datasette-cluster-map"}, {"href": "https://datasette.io/plugins/datasette-vega", "label": "datasette-vega"}]


id: plugin_hooks:plugin-hook-extra-js-urls
page: plugin_hooks
ref: plugin-hook-extra-js-urls
title: extra_js_urls(template, database, table, columns, view_name, request, datasette)
breadcrumbs: ["Plugin hooks", "Page extras"]
content:
This takes the same arguments as  extra_template_vars(...) 
                 This works in the same way as  extra_css_urls()  but for JavaScript. You can
                    return a list of URLs, a list of dictionaries or an awaitable function that returns those things: 
                 from datasette import hookimpl


@hookimpl
def extra_js_urls():
    return [
        {
            "url": "https://code.jquery.com/jquery-3.3.1.slim.min.js",
            "sri": "sha384-q8i/X+965DzO0rT7abK41JStQIAqVgRVzpbzo5smXKp4YfRvH+8abtTE1Pi6jizo",
        }
    ] 
                 You can also return URLs to files from your plugin's  static/  directory, if
                    you have one: 
                 @hookimpl
def extra_js_urls():
    return ["/-/static-plugins/your-plugin/app.js"] 
                 Note that  your-plugin  here should be the hyphenated plugin name - the name that is displayed in the list on the  /-/plugins  debug page. 
                 If your code uses  JavaScript modules  you should include the  "module": True  key. See  Custom CSS and JavaScript  for more details. 
                 @hookimpl
def extra_js_urls():
    return [
        {
            "url": "/-/static-plugins/your-plugin/app.js",
            "module": True,
        }
    ] 
                 Examples:  datasette-cluster-map ,  datasette-vega

references:
[{"href": "https://developer.mozilla.org/en-US/docs/Web/JavaScript/Guide/Modules", "label": "JavaScript modules"}, {"href": "https://datasette.io/plugins/datasette-cluster-map", "label": "datasette-cluster-map"}, {"href": "https://datasette.io/plugins/datasette-vega", "label": "datasette-vega"}]


id: plugin_hooks:plugin-hook-extra-body-script
page: plugin_hooks
ref: plugin-hook-extra-body-script
title: extra_body_script(template, database, table, columns, view_name, request, datasette)
breadcrumbs: ["Plugin hooks", "Page extras"]
references: [{"href": "https://datasette.io/plugins/datasette-cluster-map", "label": "datasette-cluster-map"}]
content:
Extra JavaScript to be added to a  <script>  block at the end of the  <body>  element on the page. 
                 This takes the same arguments as  extra_template_vars(...) 
                 The  template ,  database ,  table  and  view_name  options can be used to return different code depending on which template is being rendered and which database or table are being processed. 
                 The  datasette  instance is provided primarily so that you can consult any plugin configuration options that may have been set, using the  datasette.plugin_config(plugin_name)  method documented above. 
                 This function can return a string containing JavaScript, or a dictionary as described below, or a function or awaitable function that returns a string or dictionary. 
                 Use a dictionary if you want to specify that the code should be placed in a  <script type="module">...</script>  element: 
                 @hookimpl
def extra_body_script():
    return {
        "module": True,
        "script": "console.log('Your JavaScript goes here...')",
    } 
                 This will add the following to the end of your page: 
                 <script type="module">console.log('Your JavaScript goes here...')</script> 
                 Example:  datasette-cluster-map


id: plugin_hooks:plugin-hook-publish-subcommand
page: plugin_hooks
ref: plugin-hook-publish-subcommand
title: publish_subcommand(publish)
breadcrumbs: ["Plugin hooks"]
content:
publish  - Click publish command group 
                     
                         The Click command group for the  datasette publish  subcommand 
                     
                 
             
             This hook allows you to create new providers for the  datasette publish 
                command. Datasette uses this hook internally to implement the default  cloudrun 
                and  heroku  subcommands, so you can read
                 their source 
                to see examples of this hook in action. 
             Let's say you want to build a plugin that adds a  datasette publish my_hosting_provider --api_key=xxx mydatabase.db  publish command. Your implementation would start like this: 
             from datasette import hookimpl
from datasette.publish.common import (
    add_common_publish_arguments_and_options,
)
import click


@hookimpl
def publish_subcommand(publish):
    @publish.command()
    @add_common_publish_arguments_and_options
    @click.option(
        "-k",
        "--api_key",
        help="API key for talking to my hosting provider",
    )
    def my_hosting_provider(
        files,
        metadata,
        extra_options,
        branch,
        template_dir,
        plugins_dir,
        static,
        install,
        plugin_secret,
        version_note,
        secret,
        title,
        license,
        license_url,
        source,
        source_url,
        about,
        about_url,
        api_key,
    ): ... 
             Examples:  datasette-publish-fly ,  datasette-publish-vercel

references:
[{"href": "https://github.com/simonw/datasette/tree/main/datasette/publish", "label": "their source"}, {"href": "https://datasette.io/plugins/datasette-publish-fly", "label": "datasette-publish-fly"}, {"href": "https://datasette.io/plugins/datasette-publish-vercel", "label": "datasette-publish-vercel"}]


id: plugin_hooks:plugin-hook-render-cell
page: plugin_hooks
ref: plugin-hook-render-cell
title: render_cell(row, value, column, table, database, datasette, request)
breadcrumbs: ["Plugin hooks"]
content:
Lets you customize the display of values within table cells in the HTML table view. 
             
                 
                     row  -  sqlite.Row 
                     
                         The SQLite row object that the value being rendered is part of 
                     
                 
                 
                     value  - string, integer, float, bytes or None 
                     
                         The value that was loaded from the database 
                     
                 
                 
                     column  - string 
                     
                         The name of the column being rendered 
                     
                 
                 
                     table  - string or None 
                     
                         The name of the table - or  None  if this is a custom SQL query 
                     
                 
                 
                     database  - string 
                     
                         The name of the database 
                     
                 
                 
                     datasette  -  Datasette class 
                     
                         You can use this to access plugin configuration options via  datasette.plugin_config(your_plugin_name) , or to execute SQL queries. 
                     
                 
                 
                     request  -  Request object 
                     
                         The current request object 
                     
                 
             
             If your hook returns  None , it will be ignored. Use this to indicate that your hook is not able to custom render this particular value. 
             If the hook returns a string, that string will be rendered in the table cell. 
             If you want to return HTML markup you can do so by returning a  jinja2.Markup  object. 
             You can also return an awaitable function which returns a value. 
             Datasette will loop through all available  render_cell  hooks and display the value returned by the first one that does not return  None . 
             Here is an example of a custom  render_cell()  plugin which looks for values that are a JSON string matching the following format: 
             {"href": "https://www.example.com/", "label": "Name"} 
             If the value matches that pattern, the plugin returns an HTML link element: 
             from datasette import hookimpl
import markupsafe
import json


@hookimpl
def render_cell(value):
    # Render {"href": "...", "label": "..."} as link
    if not isinstance(value, str):
        return None
    stripped = value.strip()
    if not (
        stripped.startswith("{") and stripped.endswith("}")
    ):
        return None
    try:
        data = json.loads(value)
    except ValueError:
        return None
    if not isinstance(data, dict):
        return None
    if set(data.keys()) != {"href", "label"}:
        return None
    href = data["href"]
    if not (
        href.startswith("/")
        or href.startswith("http://")
        or href.startswith("https://")
    ):
        return None
    return markupsafe.Markup(
        '<a href="{href}">{label}</a>'.format(
            href=markupsafe.escape(data["href"]),
            label=markupsafe.escape(data["label"] or "")
            or "&nbsp;",
        )
    ) 
             Examples:  datasette-render-binary ,  datasette-render-markdown ,  datasette-json-html

references:
[{"href": "https://datasette.io/plugins/datasette-render-binary", "label": "datasette-render-binary"}, {"href": "https://datasette.io/plugins/datasette-render-markdown", "label": "datasette-render-markdown"}, {"href": "https://datasette.io/plugins/datasette-json-html", "label": "datasette-json-html"}]


id: plugin_hooks:plugin-register-output-renderer
page: plugin_hooks
ref: plugin-register-output-renderer
title: register_output_renderer(datasette)
breadcrumbs: ["Plugin hooks"]
content:
datasette  -  Datasette class 
                     
                         You can use this to access plugin configuration options via  datasette.plugin_config(your_plugin_name) 
                     
                 
             
             Registers a new output renderer, to output data in a custom format. The hook function should return a dictionary, or a list of dictionaries, of the following shape: 
             @hookimpl
def register_output_renderer(datasette):
    return {
        "extension": "test",
        "render": render_demo,
        "can_render": can_render_demo,  # Optional
    } 
             This will register  render_demo  to be called when paths with the extension  .test  (for example  /database.test ,  /database/table.test , or  /database/table/row.test ) are requested. 
             render_demo  is a Python function. It can be a regular function or an  async def render_demo()  awaitable function, depending on if it needs to make any asynchronous calls. 
             can_render_demo  is a Python function (or  async def  function) which accepts the same arguments as  render_demo  but just returns  True  or  False . It lets Datasette know if the current SQL query can be represented by the plugin - and hence influence if a link to this output format is displayed in the user interface. If you omit the  "can_render"  key from the dictionary every query will be treated as being supported by the plugin. 
             When a request is received, the  "render"  callback function is called with zero or more of the following arguments. Datasette will inspect your callback function and pass arguments that match its function signature. 
             
                 
                     datasette  -  Datasette class 
                     
                         For accessing plugin configuration and executing queries. 
                     
                 
                 
                     columns  - list of strings 
                     
                         The names of the columns returned by this query. 
                     
                 
                 
                     rows  - list of  sqlite3.Row  objects 
                     
                         The rows returned by the query. 
                     
                 
                 
                     sql  - string 
                     
                         The SQL query that was executed. 
                     
                 
                 
                     query_name  - string or None 
                     
                         If this was the execution of a  canned query , the name of that query. 
                     
                 
                 
                     database  - string 
                     
                         The name of the database. 
                     
                 
                 
                     table  - string or None 
                     
                         The table or view, if one is being rendered. 
                     
                 
                 
                     request  -  Request object 
                     
                         The current HTTP request. 
                     
                 
                 
                     error  - string or None 
                     
                         If an error occurred this string will contain the error message. 
                     
                 
                 
                     truncated  - bool or None 
                     
                         If the query response was truncated - for example a SQL query returning more than 1,000 results where pagination is not available - this will be  True . 
                     
                 
                 
                     view_name  - string 
                     
                         The name of the current view being called.  index ,  database ,  table , and  row  are the most important ones. 
                     
                 
             
             The callback function can return  None , if it is unable to render the data, or a  Response class  that will be returned to the caller. 
             It can also return a dictionary with the following keys. This format is  deprecated  as-of Datasette 0.49 and will be removed by Datasette 1.0. 
             
                 
                     body  - string or bytes, optional 
                     
                         The response body, default empty 
                     
                 
                 
                     content_type  - string, optional 
                     
                         The Content-Type header, default  text/plain 
                     
                 
                 
                     status_code  - integer, optional 
                     
                         The HTTP status code, default 200 
                     
                 
                 
                     headers  - dictionary, optional 
                     
                         Extra HTTP headers to be returned in the response. 
                     
                 
             
             An example of an output renderer callback function: 
             def render_demo():
    return Response.text("Hello World") 
             Here is a more complex example: 
             async def render_demo(datasette, columns, rows):
    db = datasette.get_database()
    result = await db.execute("select sqlite_version()")
    first_row = " | ".join(columns)
    lines = [first_row]
    lines.append("=" * len(first_row))
    for row in rows:
        lines.append(" | ".join(row))
    return Response(
        "\n".join(lines),
        content_type="text/plain; charset=utf-8",
        headers={"x-sqlite-version": result.first()[0]},
    ) 
             And here is an example  can_render  function which returns  True  only if the query results contain the columns  atom_id ,  atom_title  and  atom_updated : 
             def can_render_demo(columns):
    return {
        "atom_id",
        "atom_title",
        "atom_updated",
    }.issubset(columns) 
             Examples:  datasette-atom ,  datasette-ics ,  datasette-geojson ,  datasette-copyable

references:
[{"href": "https://datasette.io/plugins/datasette-atom", "label": "datasette-atom"}, {"href": "https://datasette.io/plugins/datasette-ics", "label": "datasette-ics"}, {"href": "https://datasette.io/plugins/datasette-geojson", "label": "datasette-geojson"}, {"href": "https://datasette.io/plugins/datasette-copyable", "label": "datasette-copyable"}]


id: plugin_hooks:plugin-register-routes
page: plugin_hooks
ref: plugin-register-routes
title: register_routes(datasette)
breadcrumbs: ["Plugin hooks"]
content:
datasette  -  Datasette class 
                     
                         You can use this to access plugin configuration options via  datasette.plugin_config(your_plugin_name) 
                     
                 
             
             Register additional view functions to execute for specified URL routes. 
             Return a list of  (regex, view_function)  pairs, something like this: 
             from datasette import hookimpl, Response
import html


async def hello_from(request):
    name = request.url_vars["name"]
    return Response.html(
        "Hello from {}".format(html.escape(name))
    )


@hookimpl
def register_routes():
    return [(r"^/hello-from/(?P<name>.*)$", hello_from)] 
             The view functions can take a number of different optional arguments. The corresponding argument will be passed to your function depending on its named parameters - a form of dependency injection. 
             The optional view function arguments are as follows: 
             
                 
                     datasette  -  Datasette class 
                     
                         You can use this to access plugin configuration options via  datasette.plugin_config(your_plugin_name) , or to execute SQL queries. 
                     
                 
                 
                     request  -  Request object 
                     
                         The current HTTP request. 
                     
                 
                 
                     scope  - dictionary 
                     
                         The incoming ASGI scope dictionary. 
                     
                 
                 
                     send  - function 
                     
                         The ASGI send function. 
                     
                 
                 
                     receive  - function 
                     
                         The ASGI receive function. 
                     
                 
             
             The view function can be a regular function or an  async def  function, depending on if it needs to use any  await  APIs. 
             The function can either return a  Response class  or it can return nothing and instead respond directly to the request using the ASGI  send  function (for advanced uses only). 
             It can also raise the  datasette.NotFound  exception to return a 404 not found error, or the  datasette.Forbidden  exception for a 403 forbidden. 
             See  Designing URLs for your plugin  for tips on designing the URL routes used by your plugin. 
             Examples:  datasette-auth-github ,  datasette-psutil

references:
[{"href": "https://datasette.io/plugins/datasette-auth-github", "label": "datasette-auth-github"}, {"href": "https://datasette.io/plugins/datasette-psutil", "label": "datasette-psutil"}]


id: plugin_hooks:plugin-hook-register-commands
page: plugin_hooks
ref: plugin-hook-register-commands
title: register_commands(cli)
breadcrumbs: ["Plugin hooks"]
content:
cli  - the root Datasette  Click command group 
                     
                         Use this to register additional CLI commands 
                     
                 
             
             Register additional CLI commands that can be run using  datsette yourcommand ... . This provides a mechanism by which plugins can add new CLI commands to Datasette. 
             This example registers a new  datasette verify file1.db file2.db  command that checks if the provided file paths are valid SQLite databases: 
             from datasette import hookimpl
import click
import sqlite3


@hookimpl
def register_commands(cli):
    @cli.command()
    @click.argument(
        "files", type=click.Path(exists=True), nargs=-1
    )
    def verify(files):
        "Verify that files can be opened by Datasette"
        for file in files:
            conn = sqlite3.connect(str(file))
            try:
                conn.execute("select * from sqlite_master")
            except sqlite3.DatabaseError:
                raise click.ClickException(
                    "Invalid database: {}".format(file)
                ) 
             The new command can then be executed like so: 
             datasette verify fixtures.db 
             Help text (from the docstring for the function plus any defined Click arguments or options) will become available using: 
             datasette verify --help 
             Plugins can register multiple commands by making multiple calls to the  @cli.command()  decorator. Consult the  Click documentation  for full details on how to build a CLI command, including how to define arguments and options. 
             Note that  register_commands()  plugins cannot used with the  --plugins-dir mechanism  - they need to be installed into the same virtual environment as Datasette using  pip install . Provided it has a  setup.py  file (see  Packaging a plugin ) you can run  pip install  directly against the directory in which you are developing your plugin like so: 
             pip install -e path/to/my/datasette-plugin 
             Examples:  datasette-auth-passwords ,  datasette-verify

references:
[{"href": "https://click.palletsprojects.com/en/latest/commands/#callback-invocation", "label": "Click command group"}, {"href": "https://click.palletsprojects.com/", "label": "Click documentation"}, {"href": "https://datasette.io/plugins/datasette-auth-passwords", "label": "datasette-auth-passwords"}, {"href": "https://datasette.io/plugins/datasette-verify", "label": "datasette-verify"}]


id: plugin_hooks:plugin-register-facet-classes
page: plugin_hooks
ref: plugin-register-facet-classes
title: register_facet_classes()
breadcrumbs: ["Plugin hooks"]
content:
Return a list of additional Facet subclasses to be registered. 
             
                 The design of this plugin hook is unstable and may change. See  issue 830 . 
             
             Each Facet subclass implements a new type of facet operation. The class should look like this: 
             class SpecialFacet(Facet):
    # This key must be unique across all facet classes:
    type = "special"

    async def suggest(self):
        # Use self.sql and self.params to suggest some facets
        suggested_facets = []
        suggested_facets.append(
            {
                "name": column,  # Or other unique name
                # Construct the URL that will enable this facet:
                "toggle_url": self.ds.absolute_url(
                    self.request,
                    path_with_added_args(
                        self.request, {"_facet": column}
                    ),
                ),
            }
        )
        return suggested_facets

    async def facet_results(self):
        # This should execute the facet operation and return results, again
        # using self.sql and self.params as the starting point
        facet_results = []
        facets_timed_out = []
        facet_size = self.get_facet_size()
        # Do some calculations here...
        for column in columns_selected_for_facet:
            try:
                facet_results_values = []
                # More calculations...
                facet_results_values.append(
                    {
                        "value": value,
                        "label": label,
                        "count": count,
                        "toggle_url": self.ds.absolute_url(
                            self.request, toggle_path
                        ),
                        "selected": selected,
                    }
                )
                facet_results.append(
                    {
                        "name": column,
                        "results": facet_results_values,
                        "truncated": len(facet_rows_results)
                        > facet_size,
                    }
                )
            except QueryInterrupted:
                facets_timed_out.append(column)

        return facet_results, facets_timed_out 
             See  datasette/facets.py  for examples of how these classes can work. 
             The plugin hook can then be used to register the new facet class like this: 
             @hookimpl
def register_facet_classes():
    return [SpecialFacet]

references:
[{"href": "https://github.com/simonw/datasette/issues/830", "label": "issue 830"}, {"href": "https://github.com/simonw/datasette/blob/main/datasette/facets.py", "label": "datasette/facets.py"}]


id: plugin_hooks:plugin-register-permissions
page: plugin_hooks
ref: plugin-register-permissions
title: register_permissions(datasette)
breadcrumbs: ["Plugin hooks"]
references: []
content:
If your plugin needs to register additional permissions unique to that plugin -  upload-csvs  for example - you can return a list of those permissions from this hook. 
             from datasette import hookimpl, Permission


@hookimpl
def register_permissions(datasette):
    return [
        Permission(
            name="upload-csvs",
            abbr=None,
            description="Upload CSV files",
            takes_database=True,
            takes_resource=False,
            default=False,
        )
    ] 
             The fields of the  Permission  class are as follows: 
             
                 
                     name  - string 
                     
                         The name of the permission, e.g.  upload-csvs . This should be unique across all plugins that the user might have installed, so choose carefully. 
                     
                 
                 
                     abbr  - string or None 
                     
                         An abbreviation of the permission, e.g.  uc . This is optional - you can set it to  None  if you do not want to pick an abbreviation. Since this needs to be unique across all installed plugins it's best not to specify an abbreviation at all. If an abbreviation is provided it will be used when creating restricted signed API tokens. 
                     
                 
                 
                     description  - string or None 
                     
                         A human-readable description of what the permission lets you do. Should make sense as the second part of a sentence that starts "A user with this permission can ...". 
                     
                 
                 
                     takes_database  - boolean 
                     
                         True  if this permission can be granted on a per-database basis,  False  if it is only valid at the overall Datasette instance level. 
                     
                 
                 
                     takes_resource  - boolean 
                     
                         True  if this permission can be granted on a per-resource basis. A resource is a database table, SQL view or  canned query . 
                     
                 
                 
                     default  - boolean 
                     
                         The default value for this permission if it is not explicitly granted to a user.  True  means the permission is granted by default,  False  means it is not. 
                         This should only be  True  if you want anonymous users to be able to take this action.


id: plugin_hooks:plugin-asgi-wrapper
page: plugin_hooks
ref: plugin-asgi-wrapper
title: asgi_wrapper(datasette)
breadcrumbs: ["Plugin hooks"]
content:
Return an  ASGI  middleware wrapper function that will be applied to the Datasette ASGI application. 
             This is a very powerful hook. You can use it to manipulate the entire Datasette response, or even to configure new URL routes that will be handled by your own custom code. 
             You can write your ASGI code directly against the low-level specification, or you can use the middleware utilities provided by an ASGI framework such as  Starlette . 
             This example plugin adds a  x-databases  HTTP header listing the currently attached databases: 
             from datasette import hookimpl
from functools import wraps


@hookimpl
def asgi_wrapper(datasette):
    def wrap_with_databases_header(app):
        @wraps(app)
        async def add_x_databases_header(
            scope, receive, send
        ):
            async def wrapped_send(event):
                if event["type"] == "http.response.start":
                    original_headers = (
                        event.get("headers") or []
                    )
                    event = {
                        "type": event["type"],
                        "status": event["status"],
                        "headers": original_headers
                        + [
                            [
                                b"x-databases",
                                ", ".join(
                                    datasette.databases.keys()
                                ).encode("utf-8"),
                            ]
                        ],
                    }
                await send(event)

            await app(scope, receive, wrapped_send)

        return add_x_databases_header

    return wrap_with_databases_header 
             Examples:  datasette-cors ,  datasette-pyinstrument ,  datasette-total-page-time

references:
[{"href": "https://asgi.readthedocs.io/", "label": "ASGI"}, {"href": "https://www.starlette.io/middleware/", "label": "Starlette"}, {"href": "https://datasette.io/plugins/datasette-cors", "label": "datasette-cors"}, {"href": "https://datasette.io/plugins/datasette-pyinstrument", "label": "datasette-pyinstrument"}, {"href": "https://datasette.io/plugins/datasette-total-page-time", "label": "datasette-total-page-time"}]


id: plugin_hooks:plugin-hook-startup
page: plugin_hooks
ref: plugin-hook-startup
title: startup(datasette)
breadcrumbs: ["Plugin hooks"]
content:
This hook fires when the Datasette application server first starts up. 
             Here is an example that validates required plugin configuration. The server will fail to start and show an error if the validation check fails: 
             @hookimpl
def startup(datasette):
    config = datasette.plugin_config("my-plugin") or {}
    assert (
        "required-setting" in config
    ), "my-plugin requires setting required-setting" 
             You can also return an async function, which will be awaited on startup. Use this option if you need to execute any database queries, for example this function which creates the  my_table  database table if it does not yet exist: 
             @hookimpl
def startup(datasette):
    async def inner():
        db = datasette.get_database()
        if "my_table" not in await db.table_names():
            await db.execute_write(
                """
                create table my_table (mycol text)
            """
            )

    return inner 
             Potential use-cases: 
             
                 
                     Run some initialization code for the plugin 
                 
                 
                     Create database tables that a plugin needs on startup 
                 
                 
                     Validate the configuration for a plugin on startup, and raise an error if it is invalid 
                 
             
             
                 If you are writing  unit tests  for a plugin that uses this hook and doesn't exercise Datasette by sending
                    any simulated requests through it you will need to explicitly call  await ds.invoke_startup()  in your tests. An example: 
                 @pytest.mark.asyncio
async def test_my_plugin():
    ds = Datasette()
    await ds.invoke_startup()
    # Rest of test goes here 
             
             Examples:  datasette-saved-queries ,  datasette-init

references:
[{"href": "https://datasette.io/plugins/datasette-saved-queries", "label": "datasette-saved-queries"}, {"href": "https://datasette.io/plugins/datasette-init", "label": "datasette-init"}]


id: plugin_hooks:plugin-hook-canned-queries
page: plugin_hooks
ref: plugin-hook-canned-queries
title: canned_queries(datasette, database, actor)
breadcrumbs: ["Plugin hooks"]
content:
datasette  -  Datasette class 
                     
                         You can use this to access plugin configuration options via  datasette.plugin_config(your_plugin_name) , or to execute SQL queries. 
                     
                 
                 
                     database  - string 
                     
                         The name of the database. 
                     
                 
                 
                     actor  - dictionary or None 
                     
                         The currently authenticated  actor . 
                     
                 
             
             Use this hook to return a dictionary of additional  canned query  definitions for the specified database. The return value should be the same shape as the JSON described in the  canned query  documentation. 
             from datasette import hookimpl


@hookimpl
def canned_queries(datasette, database):
    if database == "mydb":
        return {
            "my_query": {
                "sql": "select * from my_table where id > :min_id"
            }
        } 
             The hook can alternatively return an awaitable function that returns a list. Here's an example that returns queries that have been stored in the  saved_queries  database table, if one exists: 
             from datasette import hookimpl


@hookimpl
def canned_queries(datasette, database):
    async def inner():
        db = datasette.get_database(database)
        if await db.table_exists("saved_queries"):
            results = await db.execute(
                "select name, sql from saved_queries"
            )
            return {
                result["name"]: {"sql": result["sql"]}
                for result in results
            }

    return inner 
             The actor parameter can be used to include the currently authenticated actor in your decision. Here's an example that returns saved queries that were saved by that actor: 
             from datasette import hookimpl


@hookimpl
def canned_queries(datasette, database, actor):
    async def inner():
        db = datasette.get_database(database)
        if actor is not None and await db.table_exists(
            "saved_queries"
        ):
            results = await db.execute(
                "select name, sql from saved_queries where actor_id = :id",
                {"id": actor["id"]},
            )
            return {
                result["name"]: {"sql": result["sql"]}
                for result in results
            }

    return inner 
             Example:  datasette-saved-queries

references:
[{"href": "https://datasette.io/plugins/datasette-saved-queries", "label": "datasette-saved-queries"}]


id: plugin_hooks:plugin-hook-actor-from-request
page: plugin_hooks
ref: plugin-hook-actor-from-request
title: actor_from_request(datasette, request)
breadcrumbs: ["Plugin hooks"]
content:
datasette  -  Datasette class 
                     
                         You can use this to access plugin configuration options via  datasette.plugin_config(your_plugin_name) , or to execute SQL queries. 
                     
                 
                 
                     request  -  Request object 
                     
                         The current HTTP request. 
                     
                 
             
             This is part of Datasette's  authentication and permissions system . The function should attempt to authenticate an actor (either a user or an API actor of some sort) based on information in the request. 
             If it cannot authenticate an actor, it should return  None . Otherwise it should return a dictionary representing that actor. 
             Here's an example that authenticates the actor based on an incoming API key: 
             from datasette import hookimpl
import secrets

SECRET_KEY = "this-is-a-secret"


@hookimpl
def actor_from_request(datasette, request):
    authorization = (
        request.headers.get("authorization") or ""
    )
    expected = "Bearer {}".format(SECRET_KEY)

    if secrets.compare_digest(authorization, expected):
        return {"id": "bot"} 
             If you install this in your plugins directory you can test it like this: 
             curl -H 'Authorization: Bearer this-is-a-secret' http://localhost:8003/-/actor.json 
             Instead of returning a dictionary, this function can return an awaitable function which itself returns either  None  or a dictionary. This is useful for authentication functions that need to make a database query - for example: 
             from datasette import hookimpl


@hookimpl
def actor_from_request(datasette, request):
    async def inner():
        token = request.args.get("_token")
        if not token:
            return None
        # Look up ?_token=xxx in sessions table
        result = await datasette.get_database().execute(
            "select count(*) from sessions where token = ?",
            [token],
        )
        if result.first()[0]:
            return {"token": token}
        else:
            return None

    return inner 
             Examples:  datasette-auth-tokens ,  datasette-auth-passwords

references:
[{"href": "https://datasette.io/plugins/datasette-auth-tokens", "label": "datasette-auth-tokens"}, {"href": "https://datasette.io/plugins/datasette-auth-passwords", "label": "datasette-auth-passwords"}]


id: plugin_hooks:plugin-hook-actors-from-ids
page: plugin_hooks
ref: plugin-hook-actors-from-ids
title: actors_from_ids(datasette, actor_ids)
breadcrumbs: ["Plugin hooks"]
content:
datasette  -  Datasette class 
                     
                         You can use this to access plugin configuration options via  datasette.plugin_config(your_plugin_name) , or to execute SQL queries. 
                     
                 
                 
                     actor_ids  - list of strings or integers 
                     
                         The actor IDs to look up. 
                     
                 
             
             The hook must return a dictionary that maps the incoming actor IDs to their full dictionary representation. 
             Some plugins that implement social features may store the ID of the  actor  that performed an action - added a comment, bookmarked a table or similar - and then need a way to resolve those IDs into display-friendly actor dictionaries later on. 
             The  await datasette.actors_from_ids(actor_ids)  internal method can be used to look up actors from their IDs. It will dispatch to the first plugin that implements this hook. 
             Unlike other plugin hooks, this only uses the first implementation of the hook to return a result. You can expect users to only have a single plugin installed that implements this hook. 
             If no plugin is installed, Datasette defaults to returning actors that are just  {"id": actor_id} . 
             The hook can return a dictionary or an awaitable function that then returns a dictionary. 
             This example implementation returns actors from a database table: 
             from datasette import hookimpl


@hookimpl
def actors_from_ids(datasette, actor_ids):
    db = datasette.get_database("actors")

    async def inner():
        sql = "select id, name from actors where id in ({})".format(
            ", ".join("?" for _ in actor_ids)
        )
        actors = {}
        for row in (await db.execute(sql, actor_ids)).rows:
            actor = dict(row)
            actors[actor["id"]] = actor
        return actors

    return inner 
             The returned dictionary from this example looks like this: 
             {
    "1": {"id": "1", "name": "Tony"},
    "2": {"id": "2", "name": "Tina"},
} 
             These IDs could be integers or strings, depending on how the actors used by the Datasette instance are configured. 
             Example:  datasette-remote-actors

references:
[{"href": "https://github.com/datasette/datasette-remote-actors", "label": "datasette-remote-actors"}]


id: plugin_hooks:plugin-hook-jinja2-environment-from-request
page: plugin_hooks
ref: plugin-hook-jinja2-environment-from-request
title: jinja2_environment_from_request(datasette, request, env)
breadcrumbs: ["Plugin hooks"]
content:
datasette  -  Datasette class 
                     
                         A Datasette instance. 
                     
                 
                 
                     request  -  Request object  or  None 
                     
                         The current HTTP request, if one is available. 
                     
                 
                 
                     env  -  Environment 
                     
                         The Jinja2 environment that will be used to render the current page. 
                     
                 
             
             This hook can be used to return a customized  Jinja environment  based on the incoming request. 
             If you want to run a single Datasette instance that serves different content for different domains, you can do so like this: 
             from datasette import hookimpl
from jinja2 import ChoiceLoader, FileSystemLoader


@hookimpl
def jinja2_environment_from_request(request, env):
    if request and request.host == "www.niche-museums.com":
        return env.overlay(
            loader=ChoiceLoader(
                [
                    FileSystemLoader(
                        "/mnt/niche-museums/templates"
                    ),
                    env.loader,
                ]
            ),
            enable_async=True,
        )
    return env 
             This uses the Jinja  overlay() method  to create a new environment identical to the default environment except for having a different template loader, which first looks in the  /mnt/niche-museums/templates  directory before falling back on the default loader.

references:
[{"href": "https://jinja.palletsprojects.com/en/3.0.x/api/#jinja2.Environment", "label": "Jinja environment"}, {"href": "https://jinja.palletsprojects.com/en/3.0.x/api/#jinja2.Environment.overlay", "label": "overlay() method"}]


id: plugin_hooks:plugin-hook-filters-from-request
page: plugin_hooks
ref: plugin-hook-filters-from-request
title: filters_from_request(request, database, table, datasette)
breadcrumbs: ["Plugin hooks"]
content:
request  -  Request object 
                     
                         The current HTTP request. 
                     
                 
                 
                     database  - string 
                     
                         The name of the database. 
                     
                 
                 
                     table  - string 
                     
                         The name of the table. 
                     
                 
                 
                     datasette  -  Datasette class 
                     
                         You can use this to access plugin configuration options via  datasette.plugin_config(your_plugin_name) , or to execute SQL queries. 
                     
                 
             
             This hook runs on the  table  page, and can influence the  where  clause of the SQL query used to populate that page, based on query string arguments on the incoming request. 
             The hook should return an instance of  datasette.filters.FilterArguments  which has one required and three optional arguments: 
             return FilterArguments(
    where_clauses=["id > :max_id"],
    params={"max_id": 5},
    human_descriptions=["max_id is greater than 5"],
    extra_context={},
) 
             The arguments to the  FilterArguments  class constructor are as follows: 
             
                 
                     where_clauses  - list of strings, required 
                     
                         A list of SQL fragments that will be inserted into the SQL query, joined by the  and  operator. These can include  :named  parameters which will be populated using data in  params . 
                     
                 
                 
                     params  - dictionary, optional 
                     
                         Additional keyword arguments to be used when the query is executed. These should match any  :arguments  in the where clauses. 
                     
                 
                 
                     human_descriptions  - list of strings, optional 
                     
                         These strings will be included in the human-readable description at the top of the page and the page  <title> . 
                     
                 
                 
                     extra_context  - dictionary, optional 
                     
                         Additional context variables that should be made available to the  table.html  template when it is rendered. 
                     
                 
             
             This example plugin causes 0 results to be returned if  ?_nothing=1  is added to the URL: 
             from datasette import hookimpl
from datasette.filters import FilterArguments


@hookimpl
def filters_from_request(self, request):
    if request.args.get("_nothing"):
        return FilterArguments(
            ["1 = 0"], human_descriptions=["NOTHING"]
        ) 
             Example:  datasette-leaflet-freedraw

references:
[{"href": "https://datasette.io/plugins/datasette-leaflet-freedraw", "label": "datasette-leaflet-freedraw"}]


id: plugin_hooks:plugin-hook-permission-allowed
page: plugin_hooks
ref: plugin-hook-permission-allowed
title: permission_allowed(datasette, actor, action, resource)
breadcrumbs: ["Plugin hooks"]
content:
datasette  -  Datasette class 
                     
                         You can use this to access plugin configuration options via  datasette.plugin_config(your_plugin_name) , or to execute SQL queries. 
                     
                 
                 
                     actor  - dictionary 
                     
                         The current actor, as decided by  actor_from_request(datasette, request) . 
                     
                 
                 
                     action  - string 
                     
                         The action to be performed, e.g.  "edit-table" . 
                     
                 
                 
                     resource  - string or None 
                     
                         An identifier for the individual resource, e.g. the name of the table. 
                     
                 
             
             Called to check that an actor has permission to perform an action on a resource. Can return  True  if the action is allowed,  False  if the action is not allowed or  None  if the plugin does not have an opinion one way or the other. 
             Here's an example plugin which randomly selects if a permission should be allowed or denied, except for  view-instance  which always uses the default permission scheme instead. 
             from datasette import hookimpl
import random


@hookimpl
def permission_allowed(action):
    if action != "view-instance":
        # Return True or False at random
        return random.random() > 0.5
    # Returning None falls back to default permissions 
             This function can alternatively return an awaitable function which itself returns  True ,  False  or  None . You can use this option if you need to execute additional database queries using  await datasette.execute(...) . 
             Here's an example that allows users to view the  admin_log  table only if their actor  id  is present in the  admin_users  table. It aso disallows arbitrary SQL queries for the  staff.db  database for all users. 
             @hookimpl
def permission_allowed(datasette, actor, action, resource):
    async def inner():
        if action == "execute-sql" and resource == "staff":
            return False
        if action == "view-table" and resource == (
            "staff",
            "admin_log",
        ):
            if not actor:
                return False
            user_id = actor["id"]
            return await datasette.get_database(
                "staff"
            ).execute(
                "select count(*) from admin_users where user_id = :user_id",
                {"user_id": user_id},
            )

    return inner 
             See  built-in permissions  for a full list of permissions that are included in Datasette core. 
             Example:  datasette-permissions-sql

references:
[{"href": "https://datasette.io/plugins/datasette-permissions-sql", "label": "datasette-permissions-sql"}]


id: plugin_hooks:plugin-hook-register-magic-parameters
page: plugin_hooks
ref: plugin-hook-register-magic-parameters
title: register_magic_parameters(datasette)
breadcrumbs: ["Plugin hooks"]
references: []
content:
datasette  -  Datasette class 
                     
                         You can use this to access plugin configuration options via  datasette.plugin_config(your_plugin_name) . 
                     
                 
             
             Magic parameters  can be used to add automatic parameters to  canned queries . This plugin hook allows additional magic parameters to be defined by plugins. 
             Magic parameters all take this format:  _prefix_rest_of_parameter . The prefix indicates which magic parameter function should be called - the rest of the parameter is passed as an argument to that function. 
             To register a new function, return it as a tuple of  (string prefix, function)  from this hook. The function you register should take two arguments:  key  and  request , where  key  is the  rest_of_parameter  portion of the parameter and  request  is the current  Request object . 
             This example registers two new magic parameters:  :_request_http_version  returning the HTTP version of the current request, and  :_uuid_new  which returns a new UUID: 
             from datasette import hookimpl
from uuid import uuid4


def uuid(key, request):
    if key == "new":
        return str(uuid4())
    else:
        raise KeyError


def request(key, request):
    if key == "http_version":
        return request.scope["http_version"]
    else:
        raise KeyError


@hookimpl
def register_magic_parameters(datasette):
    return [
        ("request", request),
        ("uuid", uuid),
    ]


id: plugin_hooks:plugin-hook-forbidden
page: plugin_hooks
ref: plugin-hook-forbidden
title: forbidden(datasette, request, message)
breadcrumbs: ["Plugin hooks"]
references: []
content:
datasette  -  Datasette class 
                     
                         You can use this to access plugin configuration options via  datasette.plugin_config(your_plugin_name) , or to render templates or execute SQL queries. 
                     
                 
                 
                     request  -  Request object 
                     
                         The current HTTP request. 
                     
                 
                 
                     message  - string 
                     
                         A message hinting at why the request was forbidden. 
                     
                 
             
             Plugins can use this to customize how Datasette responds when a 403 Forbidden error occurs - usually because a page failed a permission check, see  Permissions . 
             If a plugin hook wishes to react to the error, it should return a  Response object . 
             This example returns a redirect to a  /-/login  page: 
             from datasette import hookimpl
from urllib.parse import urlencode


@hookimpl
def forbidden(request, message):
    return Response.redirect(
        "/-/login?=" + urlencode({"message": message})
    ) 
             The function can alternatively return an awaitable function if it needs to make any asynchronous method calls. This example renders a template: 
             from datasette import hookimpl, Response


@hookimpl
def forbidden(datasette):
    async def inner():
        return Response.html(
            await datasette.render_template(
                "render_message.html", request=request
            )
        )

    return inner


id: plugin_hooks:plugin-hook-handle-exception
page: plugin_hooks
ref: plugin-hook-handle-exception
title: handle_exception(datasette, request, exception)
breadcrumbs: ["Plugin hooks"]
content:
datasette  -  Datasette class 
                     
                         You can use this to access plugin configuration options via  datasette.plugin_config(your_plugin_name) , or to render templates or execute SQL queries. 
                     
                 
                 
                     request  -  Request object 
                     
                         The current HTTP request. 
                     
                 
                 
                     exception  -  Exception 
                     
                         The exception that was raised. 
                     
                 
             
             This hook is called any time an unexpected exception is raised. You can use it to record the exception. 
             If your handler returns a  Response  object it will be returned to the client in place of the default Datasette error page. 
             The handler can return a response directly, or it can return return an awaitable function that returns a response. 
             This example logs an error to  Sentry  and then renders a custom error page: 
             from datasette import hookimpl, Response
import sentry_sdk


@hookimpl
def handle_exception(datasette, exception):
    sentry_sdk.capture_exception(exception)

    async def inner():
        return Response.html(
            await datasette.render_template(
                "custom_error.html", request=request
            )
        )

    return inner 
             Example:  datasette-sentry

references:
[{"href": "https://sentry.io/", "label": "Sentry"}, {"href": "https://datasette.io/plugins/datasette-sentry", "label": "datasette-sentry"}]


id: plugin_hooks:plugin-hook-skip-csrf
page: plugin_hooks
ref: plugin-hook-skip-csrf
title: skip_csrf(datasette, scope)
breadcrumbs: ["Plugin hooks"]
content:
datasette  -  Datasette class 
                     
                         You can use this to access plugin configuration options via  datasette.plugin_config(your_plugin_name) , or to execute SQL queries. 
                     
                 
                 
                     scope  - dictionary 
                     
                         The  ASGI scope  for the incoming HTTP request. 
                     
                 
             
             This hook can be used to skip  CSRF protection  for a specific incoming request. For example, you might have a custom path at  /submit-comment  which is designed to accept comments from anywhere, whether or not the incoming request originated on the site and has an accompanying CSRF token. 
             This example will disable CSRF protection for that specific URL path: 
             from datasette import hookimpl


@hookimpl
def skip_csrf(scope):
    return scope["path"] == "/submit-comment" 
             If any of the currently active  skip_csrf()  plugin hooks return  True , CSRF protection will be skipped for the request.

references:
[{"href": "https://asgi.readthedocs.io/en/latest/specs/www.html#http-connection-scope", "label": "ASGI scope"}]


id: plugin_hooks:plugin-hook-get-metadata
page: plugin_hooks
ref: plugin-hook-get-metadata
title: get_metadata(datasette, key, database, table)
breadcrumbs: ["Plugin hooks"]
content:
datasette  -  Datasette class 
                     
                         You can use this to access plugin configuration options via  datasette.plugin_config(your_plugin_name) . 
                     
                 
                 
                     actor  - dictionary or None 
                     
                         The currently authenticated  actor . 
                     
                 
                 
                     database  - string or None 
                     
                         The name of the database metadata is being asked for. 
                     
                 
                 
                     table  - string or None 
                     
                         The name of the table. 
                     
                 
                 
                     key  - string or None 
                     
                         The name of the key for which data is being asked for. 
                     
                 
             
             This hook is responsible for returning a dictionary corresponding to Datasette  Metadata . This function is passed the  database ,  table  and  key  which were passed to the upstream internal request for metadata. Regardless, it is important to return a global metadata object, where  "databases": []  would be a top-level key. The dictionary returned here, will be merged with, and overwritten by, the contents of the physical  metadata.yaml  if one is present. 
             
                 The design of this plugin hook does not currently provide a mechanism for interacting with async code, and may change in the future. See  issue 1384 . 
             
             @hookimpl
def get_metadata(datasette, key, database, table):
    metadata = {
        "title": "This will be the Datasette landing page title!",
        "description": get_instance_description(datasette),
        "databases": [],
    }
    for db_name, db_data_dict in get_my_database_meta(
        datasette, database, table, key
    ):
        metadata["databases"][db_name] = db_data_dict
    # whatever we return here will be merged with any other plugins using this hook and
    # will be overwritten by a local metadata.yaml if one exists!
    return metadata 
             Example:  datasette-remote-metadata plugin

references:
[{"href": "https://github.com/simonw/datasette/issues/1384", "label": "issue 1384"}, {"href": "https://datasette.io/plugins/datasette-remote-metadata", "label": "datasette-remote-metadata plugin"}]


id: plugin_hooks:plugin-hook-menu-links
page: plugin_hooks
ref: plugin-hook-menu-links
title: menu_links(datasette, actor, request)
breadcrumbs: ["Plugin hooks"]
content:
datasette  -  Datasette class 
                     
                         You can use this to access plugin configuration options via  datasette.plugin_config(your_plugin_name) , or to execute SQL queries. 
                     
                 
                 
                     actor  - dictionary or None 
                     
                         The currently authenticated  actor . 
                     
                 
                 
                     request  -  Request object  or None 
                     
                         The current HTTP request. This can be  None  if the request object is not available. 
                     
                 
             
             This hook allows additional items to be included in the menu displayed by Datasette's top right menu icon. 
             The hook should return a list of  {"href": "...", "label": "..."}  menu items. These will be added to the menu. 
             It can alternatively return an  async def  awaitable function which returns a list of menu items. 
             This example adds a new menu item but only if the signed in user is  "root" : 
             from datasette import hookimpl


@hookimpl
def menu_links(datasette, actor):
    if actor and actor.get("id") == "root":
        return [
            {
                "href": datasette.urls.path(
                    "/-/edit-schema"
                ),
                "label": "Edit schema",
            },
        ] 
             Using  datasette.urls  here ensures that links in the menu will take the  base_url  setting into account. 
             Examples:  datasette-search-all ,  datasette-graphql

references:
[{"href": "https://datasette.io/plugins/datasette-search-all", "label": "datasette-search-all"}, {"href": "https://datasette.io/plugins/datasette-graphql", "label": "datasette-graphql"}]


id: plugin_hooks:plugin-actions
page: plugin_hooks
ref: plugin-actions
title: Action hooks
breadcrumbs: ["Plugin hooks"]
references: []
content:
Action hooks can be used to add items to the action menus that appear at the top of different pages within Datasette. Unlike  menu_links() , actions which are displayed on every page, actions should only be relevant to the page the user is currently viewing. 
             Each of these hooks should return return a list of  {"href": "...", "label": "..."}  menu items, with optional  "description": "..."  keys describing each action in more detail. 
             They can alternatively return an  async def  awaitable function which, when called, returns a list of those menu items.


id: plugin_hooks:plugin-hook-table-actions
page: plugin_hooks
ref: plugin-hook-table-actions
title: table_actions(datasette, actor, database, table, request)
breadcrumbs: ["Plugin hooks", "Action hooks"]
references: [{"href": "https://datasette.io/plugins/datasette-graphql", "label": "datasette-graphql"}]
content:
datasette  -  Datasette class 
                         
                             You can use this to access plugin configuration options via  datasette.plugin_config(your_plugin_name) , or to execute SQL queries. 
                         
                     
                     
                         actor  - dictionary or None 
                         
                             The currently authenticated  actor . 
                         
                     
                     
                         database  - string 
                         
                             The name of the database. 
                         
                     
                     
                         table  - string 
                         
                             The name of the table. 
                         
                     
                     
                         request  -  Request object  or None 
                         
                             The current HTTP request. This can be  None  if the request object is not available. 
                         
                     
                 
                 This example adds a new table action if the signed in user is  "root" : 
                 from datasette import hookimpl


@hookimpl
def table_actions(datasette, actor, database, table):
    if actor and actor.get("id") == "root":
        return [
            {
                "href": datasette.urls.path(
                    "/-/edit-schema/{}/{}".format(
                        database, table
                    )
                ),
                "label": "Edit schema for this table",
                "description": "Add, remove, rename or alter columns for this table.",
            }
        ] 
                 Example:  datasette-graphql


id: plugin_hooks:plugin-hook-view-actions
page: plugin_hooks
ref: plugin-hook-view-actions
title: view_actions(datasette, actor, database, view, request)
breadcrumbs: ["Plugin hooks", "Action hooks"]
references: []
content:
datasette  -  Datasette class 
                         
                             You can use this to access plugin configuration options via  datasette.plugin_config(your_plugin_name) , or to execute SQL queries. 
                         
                     
                     
                         actor  - dictionary or None 
                         
                             The currently authenticated  actor . 
                         
                     
                     
                         database  - string 
                         
                             The name of the database. 
                         
                     
                     
                         view  - string 
                         
                             The name of the SQL view. 
                         
                     
                     
                         request  -  Request object  or None 
                         
                             The current HTTP request. This can be  None  if the request object is not available. 
                         
                     
                 
                 Like  table_actions(datasette, actor, database, table, request)  but for SQL views.


id: plugin_hooks:plugin-hook-query-actions
page: plugin_hooks
ref: plugin-hook-query-actions
title: query_actions(datasette, actor, database, query_name, request, sql, params)
breadcrumbs: ["Plugin hooks", "Action hooks"]
references: [{"href": "https://datasette.io/plugins/datasette-create-view", "label": "datasette-create-view"}]
content:
datasette  -  Datasette class 
                         
                             You can use this to access plugin configuration options via  datasette.plugin_config(your_plugin_name) , or to execute SQL queries. 
                         
                     
                     
                         actor  - dictionary or None 
                         
                             The currently authenticated  actor . 
                         
                     
                     
                         database  - string 
                         
                             The name of the database. 
                         
                     
                     
                         query_name  - string or None 
                         
                             The name of the canned query, or  None  if this is an arbitrary SQL query. 
                         
                     
                     
                         request  -  Request object 
                         
                             The current HTTP request. 
                         
                     
                     
                         sql  - string 
                         
                             The SQL query being executed 
                         
                     
                     
                         params  - dictionary 
                         
                             The parameters passed to the SQL query, if any. 
                         
                     
                 
                 Populates a "Query actions" menu on the canned query and arbitrary SQL query pages. 
                 This example adds a new query action linking to a page for explaining a query: 
                 from datasette import hookimpl
import urllib


@hookimpl
def query_actions(datasette, database, query_name, sql):
    # Don't explain an explain
    if sql.lower().startswith("explain"):
        return
    return [
        {
            "href": datasette.urls.database(database)
            + "?"
            + urllib.parse.urlencode(
                {
                    "sql": "explain " + sql,
                }
            ),
            "label": "Explain this query",
            "description": "Get a summary of how SQLite executes the query",
        },
    ] 
                 Example:  datasette-create-view


id: plugin_hooks:plugin-hook-row-actions
page: plugin_hooks
ref: plugin-hook-row-actions
title: row_actions(datasette, actor, request, database, table, row)
breadcrumbs: ["Plugin hooks", "Action hooks"]
references: [{"href": "https://datasette.io/plugins/datasette-enrichments", "label": "datasette-enrichments"}]
content:
datasette  -  Datasette class 
                         
                             You can use this to access plugin configuration options via  datasette.plugin_config(your_plugin_name) , or to execute SQL queries. 
                         
                     
                     
                         actor  - dictionary or None 
                         
                             The currently authenticated  actor . 
                         
                     
                     
                         request  -  Request object  or None 
                         
                             The current HTTP request. 
                         
                     
                     
                         database  - string 
                         
                             The name of the database. 
                         
                     
                     
                         table  - string 
                         
                             The name of the table. 
                         
                     
                     
                         row  -  sqlite.Row 
                         
                             The SQLite row object being displayed on the page. 
                         
                     
                 
                 Return links for the "Row actions" menu shown at the top of the row page. 
                 This example displays the row in JSON plus some additional debug information if the user is signed in: 
                 from datasette import hookimpl


@hookimpl
def row_actions(datasette, database, table, actor, row):
    if actor:
        return [
            {
                "href": datasette.urls.instance(),
                "label": f"Row details for {actor['id']}",
                "description": json.dumps(
                    dict(row), default=repr
                ),
            },
        ] 
                 Example:  datasette-enrichments


id: plugin_hooks:plugin-hook-database-actions
page: plugin_hooks
ref: plugin-hook-database-actions
title: database_actions(datasette, actor, database, request)
breadcrumbs: ["Plugin hooks", "Action hooks"]
content:
datasette  -  Datasette class 
                         
                             You can use this to access plugin configuration options via  datasette.plugin_config(your_plugin_name) , or to execute SQL queries. 
                         
                     
                     
                         actor  - dictionary or None 
                         
                             The currently authenticated  actor . 
                         
                     
                     
                         database  - string 
                         
                             The name of the database. 
                         
                     
                     
                         request  -  Request object 
                         
                             The current HTTP request. 
                         
                     
                 
                 Populates an actions menu on the database page. 
                 This example adds a new database action for creating a table, if the user has the  edit-schema  permission: 
                 from datasette import hookimpl


@hookimpl
def database_actions(datasette, actor, database):
    async def inner():
        if not await datasette.permission_allowed(
            actor,
            "edit-schema",
            resource=database,
            default=False,
        ):
            return []
        return [
            {
                "href": datasette.urls.path(
                    "/-/edit-schema/{}/-/create".format(
                        database
                    )
                ),
                "label": "Create a table",
            }
        ]

    return inner 
                 Example:  datasette-graphql ,  datasette-edit-schema

references:
[{"href": "https://datasette.io/plugins/datasette-graphql", "label": "datasette-graphql"}, {"href": "https://datasette.io/plugins/datasette-edit-schema", "label": "datasette-edit-schema"}]


id: plugin_hooks:plugin-hook-homepage-actions
page: plugin_hooks
ref: plugin-hook-homepage-actions
title: homepage_actions(datasette, actor, request)
breadcrumbs: ["Plugin hooks", "Action hooks"]
references: []
content:
datasette  -  Datasette class 
                         
                             You can use this to access plugin configuration options via  datasette.plugin_config(your_plugin_name) , or to execute SQL queries. 
                         
                     
                     
                         actor  - dictionary or None 
                         
                             The currently authenticated  actor . 
                         
                     
                     
                         request  -  Request object 
                         
                             The current HTTP request. 
                         
                     
                 
                 Populates an actions menu on the top-level index homepage of the Datasette instance. 
                 This example adds a link an imagined tool for editing the homepage, only for signed in users: 
                 from datasette import hookimpl


@hookimpl
def homepage_actions(datasette, actor):
    if actor:
        return [
            {
                "href": datasette.urls.path(
                    "/-/customize-homepage"
                ),
                "label": "Customize homepage",
            }
        ]


id: plugin_hooks:plugin-hook-slots
page: plugin_hooks
ref: plugin-hook-slots
title: Template slots
breadcrumbs: ["Plugin hooks"]
content:
The following set of plugin hooks can be used to return extra HTML content that will be inserted into the corresponding page, directly below the  <h1>  heading. 
             Multiple plugins can contribute content here. The order in which it is displayed can be controlled using Pluggy's  call time order options . 
             Each of these plugin hooks can return either a string or an awaitable function that returns a string.

references:
[{"href": "https://pluggy.readthedocs.io/en/stable/#call-time-order", "label": "call time order options"}]


id: plugin_hooks:plugin-hook-top-homepage
page: plugin_hooks
ref: plugin-hook-top-homepage
title: top_homepage(datasette, request)
breadcrumbs: ["Plugin hooks", "Template slots"]
references: []
content:
datasette  -  Datasette class 
                         
                             You can use this to access plugin configuration options via  datasette.plugin_config(your_plugin_name) . 
                         
                     
                     
                         request  -  Request object 
                         
                             The current HTTP request. 
                         
                     
                 
                 Returns HTML to be displayed at the top of the Datasette homepage.


id: plugin_hooks:plugin-hook-top-database
page: plugin_hooks
ref: plugin-hook-top-database
title: top_database(datasette, request, database)
breadcrumbs: ["Plugin hooks", "Template slots"]
references: []
content:
datasette  -  Datasette class 
                         
                             You can use this to access plugin configuration options via  datasette.plugin_config(your_plugin_name) . 
                         
                     
                     
                         request  -  Request object 
                         
                             The current HTTP request. 
                         
                     
                     
                         database  - string 
                         
                             The name of the database. 
                         
                     
                 
                 Returns HTML to be displayed at the top of the database page.


id: plugin_hooks:plugin-hook-top-table
page: plugin_hooks
ref: plugin-hook-top-table
title: top_table(datasette, request, database, table)
breadcrumbs: ["Plugin hooks", "Template slots"]
references: []
content:
datasette  -  Datasette class 
                         
                             You can use this to access plugin configuration options via  datasette.plugin_config(your_plugin_name) . 
                         
                     
                     
                         request  -  Request object 
                         
                             The current HTTP request. 
                         
                     
                     
                         database  - string 
                         
                             The name of the database. 
                         
                     
                     
                         table  - string 
                         
                             The name of the table. 
                         
                     
                 
                 Returns HTML to be displayed at the top of the table page.


id: plugin_hooks:plugin-hook-top-row
page: plugin_hooks
ref: plugin-hook-top-row
title: top_row(datasette, request, database, table, row)
breadcrumbs: ["Plugin hooks", "Template slots"]
references: []
content:
datasette  -  Datasette class 
                         
                             You can use this to access plugin configuration options via  datasette.plugin_config(your_plugin_name) . 
                         
                     
                     
                         request  -  Request object 
                         
                             The current HTTP request. 
                         
                     
                     
                         database  - string 
                         
                             The name of the database. 
                         
                     
                     
                         table  - string 
                         
                             The name of the table. 
                         
                     
                     
                         row  -  sqlite.Row 
                         
                             The SQLite row object being displayed. 
                         
                     
                 
                 Returns HTML to be displayed at the top of the row page.


id: plugin_hooks:plugin-hook-top-query
page: plugin_hooks
ref: plugin-hook-top-query
title: top_query(datasette, request, database, sql)
breadcrumbs: ["Plugin hooks", "Template slots"]
references: []
content:
datasette  -  Datasette class 
                         
                             You can use this to access plugin configuration options via  datasette.plugin_config(your_plugin_name) . 
                         
                     
                     
                         request  -  Request object 
                         
                             The current HTTP request. 
                         
                     
                     
                         database  - string 
                         
                             The name of the database. 
                         
                     
                     
                         sql  - string 
                         
                             The SQL query. 
                         
                     
                 
                 Returns HTML to be displayed at the top of the query results page.


id: plugin_hooks:plugin-hook-top-canned-query
page: plugin_hooks
ref: plugin-hook-top-canned-query
title: top_canned_query(datasette, request, database, query_name)
breadcrumbs: ["Plugin hooks", "Template slots"]
references: []
content:
datasette  -  Datasette class 
                         
                             You can use this to access plugin configuration options via  datasette.plugin_config(your_plugin_name) . 
                         
                     
                     
                         request  -  Request object 
                         
                             The current HTTP request. 
                         
                     
                     
                         database  - string 
                         
                             The name of the database. 
                         
                     
                     
                         query_name  - string 
                         
                             The name of the canned query. 
                         
                     
                 
                 Returns HTML to be displayed at the top of the canned query page.


id: plugin_hooks:plugin-event-tracking
page: plugin_hooks
ref: plugin-event-tracking
title: Event tracking
breadcrumbs: ["Plugin hooks"]
references: []
content:
Datasette includes an internal mechanism for tracking notable events. This can be used for analytics, but can also be used by plugins that want to listen out for when key events occur (such as a table being created) and take action in response. 
             Plugins can register to receive events using the  track_event  plugin hook. 
             They can also define their own events for other plugins to receive using the  register_events() plugin hook , combined with calls to the  datasette.track_event() internal method .


id: plugin_hooks:plugin-hook-track-event
page: plugin_hooks
ref: plugin-hook-track-event
title: track_event(datasette, event)
breadcrumbs: ["Plugin hooks", "Event tracking"]
references: [{"href": "https://datasette.io/plugins/datasette-events-db", "label": "datasette-events-db"}]
content:
datasette  -  Datasette class 
                         
                             You can use this to access plugin configuration options via  datasette.plugin_config(your_plugin_name) . 
                         
                     
                     
                         event  -  Event 
                         
                             Information about the event, represented as an instance of a subclass of the  Event  base class. 
                         
                     
                 
                 This hook will be called any time an event is tracked by code that calls the  datasette.track_event(...)  internal method. 
                 The  event  object will always have the following properties: 
                 
                     
                         name : a string representing the name of the event, for example  logout  or  create-table . 
                     
                     
                         actor : a dictionary representing the actor that triggered the event, or  None  if the event was not triggered by an actor. 
                     
                     
                         created : a  datatime.datetime  object in the  timezone.utc  timezone representing the time the event object was created. 
                     
                 
                 Other properties on the event will be available depending on the type of event. You can also access those as a dictionary using  event.properties() . 
                 The events fired by Datasette core are  documented here . 
                 This example plugin logs details of all events to standard error: 
                 from datasette import hookimpl
import json
import sys


@hookimpl
def track_event(event):
    name = event.name
    actor = event.actor
    properties = event.properties()
    msg = json.dumps(
        {
            "name": name,
            "actor": actor,
            "properties": properties,
        }
    )
    print(msg, file=sys.stderr, flush=True) 
                 The function can also return an async function which will be awaited. This is useful for writing to a database. 
                 This example logs events to a  datasette_events  table in a database called  events . It uses the  startup()  hook to create that table if it does not exist. 
                 from datasette import hookimpl
import json

@hookimpl
def startup(datasette):
    async def inner():
        db = datasette.get_database("events")
        await db.execute_write(
            """
            create table if not exists datasette_events (
                id integer primary key,
                event_type text,
                created text,
                actor text,
                properties text
            )
        """
        )

    return inner


@hookimpl
def track_event(datasette, event):
    async def inner():
        db = datasette.get_database("events")
        properties = event.properties()
        await db.execute_write(
            """
            insert into datasette_events (event_type, created, actor, properties)
            values (?, strftime('%Y-%m-%d %H:%M:%S', 'now'), ?, ?)
        """,
            (event.name, json.dumps(event.actor), json.dumps(properties)),
        )

    return inner 
                 Example:  datasette-events-db


id: plugin_hooks:plugin-hook-register-events
page: plugin_hooks
ref: plugin-hook-register-events
title: register_events(datasette)
breadcrumbs: ["Plugin hooks", "Event tracking"]
references: []
content:
datasette  -  Datasette class 
                         
                             You can use this to access plugin configuration options via  datasette.plugin_config(your_plugin_name) . 
                         
                     
                 
                 This hook should return a list of  Event  subclasses that represent custom events that the plugin might send to the  datasette.track_event()  method. 
                 This example registers event subclasses for  ban-user  and  unban-user  events: 
                 from dataclasses import dataclass
from datasette import hookimpl, Event


@dataclass
class BanUserEvent(Event):
    name = "ban-user"
    user: dict


@dataclass
class UnbanUserEvent(Event):
    name = "unban-user"
    user: dict


@hookimpl
def register_events():
    return [BanUserEvent, UnbanUserEvent] 
                 The plugin can then call  datasette.track_event(...)  to send a  ban-user  event: 
                 await datasette.track_event(
    BanUserEvent(user={"id": 1, "username": "cleverbot"})
)


id: binary_data:binary
page: binary_data
ref: binary
title: Binary data
breadcrumbs: []
content:
SQLite tables can contain binary data in  BLOB  columns. 
         Datasette includes special handling for these binary values. The Datasette interface detects binary values and provides a link to download their content, for example on  https://latest.datasette.io/fixtures/binary_data 
         
         Binary data is represented in  .json  exports using Base64 encoding. 
         https://latest.datasette.io/fixtures/binary_data.json?_shape=array 
         [
    {
        "rowid": 1,
        "data": {
            "$base64": true,
            "encoded": "FRwCx60F/g=="
        }
    },
    {
        "rowid": 2,
        "data": {
            "$base64": true,
            "encoded": "FRwDx60F/g=="
        }
    },
    {
        "rowid": 3,
        "data": null
    }
]

references:
[{"href": "https://latest.datasette.io/fixtures/binary_data", "label": "https://latest.datasette.io/fixtures/binary_data"}, {"href": "https://latest.datasette.io/fixtures/binary_data.json?_shape=array", "label": "https://latest.datasette.io/fixtures/binary_data.json?_shape=array"}]


id: binary_data:binary-linking
page: binary_data
ref: binary-linking
title: Linking to binary downloads
breadcrumbs: ["Binary data"]
content:
The  .blob  output format is used to return binary data. It requires a  _blob_column=  query string argument specifying which BLOB column should be downloaded, for example: 
             https://latest.datasette.io/fixtures/binary_data/1.blob?_blob_column=data 
             This output format can also be used to return binary data from an arbitrary SQL query. Since such queries do not specify an exact row, an additional  ?_blob_hash=  parameter can be used to specify the SHA-256 hash of the value that is being linked to. 
             Consider the query  select data from binary_data  -  demonstrated here . 
             That page links to the binary value downloads. Those links look like this: 
             https://latest.datasette.io/fixtures.blob?sql=select+data+from+binary_data&_blob_column=data&_blob_hash=f3088978da8f9aea479ffc7f631370b968d2e855eeb172bea7f6c7a04262bb6d 
             These  .blob  links are also returned in the  .csv  exports Datasette provides for binary tables and queries, since the CSV format does not have a mechanism for representing binary data.

references:
[{"href": "https://latest.datasette.io/fixtures/binary_data/1.blob?_blob_column=data", "label": "https://latest.datasette.io/fixtures/binary_data/1.blob?_blob_column=data"}, {"href": "https://latest.datasette.io/fixtures?sql=select+data+from+binary_data", "label": "demonstrated here"}, {"href": "https://latest.datasette.io/fixtures.blob?sql=select+data+from+binary_data&_blob_column=data&_blob_hash=f3088978da8f9aea479ffc7f631370b968d2e855eeb172bea7f6c7a04262bb6d", "label": "https://latest.datasette.io/fixtures.blob?sql=select+data+from+binary_data&_blob_column=data&_blob_hash=f3088978da8f9aea479ffc7f631370b968d2e855eeb172bea7f6c7a04262bb6d"}]


id: binary_data:binary-plugins
page: binary_data
ref: binary-plugins
title: Binary plugins
breadcrumbs: ["Binary data"]
content:
Several Datasette plugins are available that change the way Datasette treats binary data. 
             
                 
                     datasette-render-binary  modifies Datasette's default interface to show an automatic guess at what type of binary data is being stored, along with a visual representation of the binary value that displays ASCII strings directly in the interface. 
                 
                 
                     datasette-render-images  detects common image formats and renders them as images directly in the Datasette interface. 
                 
                 
                     datasette-media  allows Datasette interfaces to be configured to serve binary files from configured SQL queries, and includes the ability to resize images directly before serving them.

references:
[{"href": "https://github.com/simonw/datasette-render-binary", "label": "datasette-render-binary"}, {"href": "https://github.com/simonw/datasette-render-images", "label": "datasette-render-images"}, {"href": "https://github.com/simonw/datasette-media", "label": "datasette-media"}]


id: contributing:id1
page: contributing
ref: id1
title: Contributing
breadcrumbs: []
references: []
content:
Datasette is an open source project. We welcome contributions! 
         This document describes how to contribute to Datasette core. You can also contribute to the wider Datasette ecosystem by creating new  Plugins .


id: contributing:general-guidelines
page: contributing
ref: general-guidelines
title: General guidelines
breadcrumbs: ["Contributing"]
references: []
content:
main should always be releasable . Incomplete features should live in branches. This ensures that any small bug fixes can be quickly released. 
                 
                 
                     The ideal commit  should bundle together the implementation, unit tests and associated documentation updates. The commit message should link to an associated issue. 
                 
                 
                     New plugin hooks  should only be shipped if accompanied by a separate release of a non-demo plugin that uses them.


id: contributing:devenvironment
page: contributing
ref: devenvironment
title: Setting up a development environment
breadcrumbs: ["Contributing"]
content:
If you have Python 3.8 or higher installed on your computer (on OS X the quickest way to do this  is using homebrew ) you can install an editable copy of Datasette using the following steps. 
             If you want to use GitHub to publish your changes, first  create a fork of datasette  under your own GitHub account. 
             Now clone that repository somewhere on your computer: 
             git clone git@github.com:YOURNAME/datasette 
             If you want to get started without creating your own fork, you can do this instead: 
             git clone git@github.com:simonw/datasette 
             The next step is to create a virtual environment for your project and use it to install Datasette's dependencies: 
             cd datasette
# Create a virtual environment in ./venv
python3 -m venv ./venv
# Now activate the virtual environment, so pip can install into it
source venv/bin/activate
# Install Datasette and its testing dependencies
python3 -m pip install -e '.[test]' 
             That last line does most of the work:  pip install -e  means "install this package in a way that allows me to edit the source code in place". The  .[test]  option means "use the setup.py in this directory and install the optional testing dependencies as well".

references:
[{"href": "https://docs.python-guide.org/starting/install3/osx/", "label": "is using homebrew"}, {"href": "https://github.com/simonw/datasette/fork", "label": "create a fork of datasette"}]


id: contributing:contributing-running-tests
page: contributing
ref: contributing-running-tests
title: Running the tests
breadcrumbs: ["Contributing"]
content:
Once you have done this, you can run the Datasette unit tests from inside your  datasette/  directory using  pytest  like so: 
             pytest 
             You can run the tests faster using multiple CPU cores with  pytest-xdist  like this: 
             pytest -n auto -m "not serial" 
             -n auto  detects the number of available cores automatically. The  -m "not serial"  skips tests that don't work well in a parallel test environment. You can run those tests separately like so: 
             pytest -m "serial"

references:
[{"href": "https://docs.pytest.org/", "label": "pytest"}, {"href": "https://pypi.org/project/pytest-xdist/", "label": "pytest-xdist"}]


id: contributing:contributing-using-fixtures
page: contributing
ref: contributing-using-fixtures
title: Using fixtures
breadcrumbs: ["Contributing"]
references: []
content:
To run Datasette itself, type  datasette . 
             You're going to need at least one SQLite database. A quick way to get started is to use the fixtures database that Datasette uses for its own tests. 
             You can create a copy of that database by running this command: 
             python tests/fixtures.py fixtures.db 
             Now you can run Datasette against the new fixtures database like so: 
             datasette fixtures.db 
             This will start a server at  http://127.0.0.1:8001/ . 
             Any changes you make in the  datasette/templates  or  datasette/static  folder will be picked up immediately (though you may need to do a force-refresh in your browser to see changes to CSS or JavaScript). 
             If you want to change Datasette's Python code you can use the  --reload  option to cause Datasette to automatically reload any time the underlying code changes: 
             datasette --reload fixtures.db 
             You can also use the  fixtures.py  script to recreate the testing version of  metadata.json  used by the unit tests. To do that: 
             python tests/fixtures.py fixtures.db fixtures-metadata.json 
             Or to output the plugins used by the tests, run this: 
             python tests/fixtures.py fixtures.db fixtures-metadata.json fixtures-plugins
Test tables written to fixtures.db
- metadata written to fixtures-metadata.json
Wrote plugin: fixtures-plugins/register_output_renderer.py
Wrote plugin: fixtures-plugins/view_name.py
Wrote plugin: fixtures-plugins/my_plugin.py
Wrote plugin: fixtures-plugins/messages_output_renderer.py
Wrote plugin: fixtures-plugins/my_plugin_2.py 
             Then run Datasette like this: 
             datasette fixtures.db -m fixtures-metadata.json --plugins-dir=fixtures-plugins/


id: contributing:contributing-debugging
page: contributing
ref: contributing-debugging
title: Debugging
breadcrumbs: ["Contributing"]
references: []
content:
Any errors that occur while Datasette is running while display a stack trace on the console. 
             You can tell Datasette to open an interactive  pdb  debugger session if an error occurs using the  --pdb  option: 
             datasette --pdb fixtures.db


id: contributing:contributing-formatting
page: contributing
ref: contributing-formatting
title: Code formatting
breadcrumbs: ["Contributing"]
content:
Datasette uses opinionated code formatters:  Black  for Python and  Prettier  for JavaScript. 
             These formatters are enforced by Datasette's continuous integration: if a commit includes Python or JavaScript code that does not match the style enforced by those tools, the tests will fail. 
             When developing locally, you can verify and correct the formatting of your code using these tools.

references:
[{"href": "https://github.com/psf/black", "label": "Black"}, {"href": "https://prettier.io/", "label": "Prettier"}]


id: contributing:contributing-formatting-black
page: contributing
ref: contributing-formatting-black
title: Running Black
breadcrumbs: ["Contributing", "Code formatting"]
references: []
content:
Black will be installed when you run  pip install -e '.[test]' . To test that your code complies with Black, run the following in your root  datasette  repository checkout: 
                 black . --check 
                 All done! ✨ 🍰 ✨
95 files would be left unchanged. 
                 If any of your code does not conform to Black you can run this to automatically fix those problems: 
                 black . 
                 reformatted ../datasette/setup.py
All done! ✨ 🍰 ✨
1 file reformatted, 94 files left unchanged.


id: contributing:contributing-formatting-blacken-docs
page: contributing
ref: contributing-formatting-blacken-docs
title: blacken-docs
breadcrumbs: ["Contributing", "Code formatting"]
references: [{"href": "https://pypi.org/project/blacken-docs/", "label": "blacken-docs"}]
content:
The  blacken-docs  command applies Black formatting rules to code examples in the documentation. Run it like this: 
                 blacken-docs -l 60 docs/*.rst


id: contributing:contributing-formatting-prettier
page: contributing
ref: contributing-formatting-prettier
title: Prettier
breadcrumbs: ["Contributing", "Code formatting"]
references: [{"href": "https://nodejs.org/en/download/package-manager/", "label": "install Node.js"}]
content:
To install Prettier,  install Node.js  and then run the following in the root of your  datasette  repository checkout: 
                 npm install 
                 This will install Prettier in a  node_modules  directory. You can then check that your code matches the coding style like so: 
                 npm run prettier -- --check 
                 > prettier
> prettier 'datasette/static/*[!.min].js' "--check"

Checking formatting...
[warn] datasette/static/plugins.js
[warn] Code style issues found in the above file(s). Forgot to run Prettier? 
                 You can fix any problems by running: 
                 npm run fix


id: contributing:contributing-documentation
page: contributing
ref: contributing-documentation
title: Editing and building the documentation
breadcrumbs: ["Contributing"]
content:
Datasette's documentation lives in the  docs/  directory and is deployed automatically using  Read The Docs . 
             The documentation is written using reStructuredText. You may find this article on  The subset of reStructuredText worth committing to memory  useful. 
             You can build it locally by installing  sphinx  and  sphinx_rtd_theme  in your Datasette development environment and then running  make html  directly in the  docs/  directory: 
             # You may first need to activate your virtual environment:
source venv/bin/activate

# Install the dependencies needed to build the docs
pip install -e .[docs]

# Now build the docs
cd docs/
make html 
             This will create the HTML version of the documentation in  docs/_build/html . You can open it in your browser like so: 
             open _build/html/index.html 
             Any time you make changes to a  .rst  file you can re-run  make html  to update the built documents, then refresh them in your browser. 
             For added productivity, you can use use  sphinx-autobuild  to run Sphinx in auto-build mode. This will run a local webserver serving the docs that automatically rebuilds them and refreshes the page any time you hit save in your editor. 
             sphinx-autobuild  will have been installed when you ran  pip install -e .[docs] . In your  docs/  directory you can start the server by running the following: 
             make livehtml 
             Now browse to  http://localhost:8000/  to view the documentation. Any edits you make should be instantly reflected in your browser.

references:
[{"href": "https://readthedocs.org/", "label": "Read The Docs"}, {"href": "https://simonwillison.net/2018/Aug/25/restructuredtext/", "label": "The subset of reStructuredText worth committing to memory"}, {"href": "https://pypi.org/project/sphinx-autobuild/", "label": "sphinx-autobuild"}]


id: contributing:contributing-documentation-cog
page: contributing
ref: contributing-documentation-cog
title: Running Cog
breadcrumbs: ["Contributing", "Editing and building the documentation"]
references: [{"href": "https://github.com/nedbat/cog", "label": "Cog"}]
content:
Some pages of documentation (in particular the  CLI reference ) are automatically updated using  Cog . 
                 To update these pages, run the following command: 
                 cog -r docs/*.rst


id: contributing:contributing-continuous-deployment
page: contributing
ref: contributing-continuous-deployment
title: Continuously deployed demo instances
breadcrumbs: ["Contributing"]
content:
The demo instance at  latest.datasette.io  is re-deployed automatically to Google Cloud Run for every push to  main  that passes the test suite. This is implemented by the GitHub Actions workflow at  .github/workflows/deploy-latest.yml . 
             Specific branches can also be set to automatically deploy by adding them to the  on: push: branches  block at the top of the workflow YAML file. Branches configured in this way will be deployed to a new Cloud Run service whether or not their tests pass. 
             The Cloud Run URL for a branch demo can be found in the GitHub Actions logs.

references:
[{"href": "https://latest.datasette.io/", "label": "latest.datasette.io"}, {"href": "https://github.com/simonw/datasette/blob/main/.github/workflows/deploy-latest.yml", "label": ".github/workflows/deploy-latest.yml"}]


id: contributing:contributing-release
page: contributing
ref: contributing-release
title: Release process
breadcrumbs: ["Contributing"]
content:
Datasette releases are performed using tags. When a new release is published on GitHub, a  GitHub Action workflow  will perform the following: 
             
                 
                     Run the unit tests against all supported Python versions. If the tests pass... 
                 
                 
                     Build a Docker image of the release and push a tag to  https://hub.docker.com/r/datasetteproject/datasette 
                 
                 
                     Re-point the "latest" tag on Docker Hub to the new image 
                 
                 
                     Build a wheel bundle of the underlying Python source code 
                 
                 
                     Push that new wheel up to PyPI:  https://pypi.org/project/datasette/ 
                 
                 
                     If the release is an alpha, navigate to  https://readthedocs.org/projects/datasette/versions/  and search for the tag name in the "Activate a version" filter, then mark that version as "active" to ensure it will appear on the public ReadTheDocs documentation site. 
                 
             
             To deploy new releases you will need to have push access to the main Datasette GitHub repository. 
             Datasette follows  Semantic Versioning : 
             major.minor.patch 
             We increment  major  for backwards-incompatible releases. Datasette is currently pre-1.0 so the major version is always  0 . 
             We increment  minor  for new features. 
             We increment  patch  for bugfix releass. 
             Alpha and beta releases  may have an additional  a0  or  b0  prefix - the integer component will be incremented with each subsequent alpha or beta. 
             To release a new version, first create a commit that updates the version number in  datasette/version.py  and the  the changelog  with highlights of the new version. An example  commit can be seen here : 
             # Update changelog
git commit -m " Release 0.51a1

Refs #1056, #1039, #998, #1045, #1033, #1036, #1034, #976, #1057, #1058, #1053, #1064, #1066" -a
git push 
             Referencing the issues that are part of the release in the commit message ensures the name of the release shows up on those issue pages, e.g.  here . 
             You can generate the list of issue references for a specific release by copying and pasting text from the release notes or GitHub changes-since-last-release view into this  Extract issue numbers from pasted text  tool. 
             To create the tag for the release, create  a new release  on GitHub matching the new version number. You can convert the release notes to Markdown by copying and pasting the rendered HTML into this  Paste to Markdown tool . 
             Finally, post a news item about the release on  datasette.io  by editing the  news.yaml  file in that site's repository.

references:
[{"href": "https://github.com/simonw/datasette/blob/main/.github/workflows/deploy-latest.yml", "label": "GitHub Action workflow"}, {"href": "https://hub.docker.com/r/datasetteproject/datasette", "label": "https://hub.docker.com/r/datasetteproject/datasette"}, {"href": "https://pypi.org/project/datasette/", "label": "https://pypi.org/project/datasette/"}, {"href": "https://readthedocs.org/projects/datasette/versions/", "label": "https://readthedocs.org/projects/datasette/versions/"}, {"href": "https://semver.org/", "label": "Semantic Versioning"}, {"href": "https://github.com/simonw/datasette/commit/0e1e89c6ba3d0fbdb0823272952cf356f3016def", "label": "commit can be seen here"}, {"href": "https://github.com/simonw/datasette/issues/581#ref-commit-d56f402", "label": "here"}, {"href": "https://observablehq.com/@simonw/extract-issue-numbers-from-pasted-text", "label": "Extract issue numbers from pasted text"}, {"href": "https://github.com/simonw/datasette/releases/new", "label": "a new release"}, {"href": "https://euangoddard.github.io/clipboard2markdown/", "label": "Paste to Markdown tool"}, {"href": "https://datasette.io/", "label": "datasette.io"}, {"href": "https://github.com/simonw/datasette.io/blob/main/news.yaml", "label": "news.yaml"}]


id: contributing:contributing-alpha-beta
page: contributing
ref: contributing-alpha-beta
title: Alpha and beta releases
breadcrumbs: ["Contributing"]
content:
Alpha and beta releases are published to preview upcoming features that may not yet be stable - in particular to preview new plugin hooks. 
             You are welcome to try these out, but please be aware that details may change before the final release. 
             Please join  discussions on the issue tracker  to share your thoughts and experiences with on alpha and beta features that you try out.

references:
[{"href": "https://github.com/simonw/datasette/issues", "label": "discussions on the issue tracker"}]


id: contributing:contributing-bug-fix-branch
page: contributing
ref: contributing-bug-fix-branch
title: Releasing bug fixes from a branch
breadcrumbs: ["Contributing"]
references: [{"href": "https://github.com/simonw/datasette/releases/new", "label": "Draft a new release"}]
content:
If it's necessary to publish a bug fix release without shipping new features that have landed on  main  a release branch can be used. 
             Create it from the relevant last tagged release like so: 
             git branch 0.52.x 0.52.4
git checkout 0.52.x 
             Next cherry-pick the commits containing the bug fixes: 
             git cherry-pick COMMIT 
             Write the release notes in the branch, and update the version number in  version.py . Then push the branch: 
             git push -u origin 0.52.x 
             Once the tests have completed, publish the release from that branch target using the GitHub  Draft a new release  form. 
             Finally, cherry-pick the commit with the release notes and version number bump across to  main : 
             git checkout main
git cherry-pick COMMIT
git push


id: contributing:contributing-upgrading-codemirror
page: contributing
ref: contributing-upgrading-codemirror
title: Upgrading CodeMirror
breadcrumbs: ["Contributing"]
content:
Datasette bundles  CodeMirror  for the SQL editing interface, e.g. on  this page . Here are the steps for upgrading to a new version of CodeMirror: 
             
                 
                     Install the packages with: 
                     npm i codemirror @codemirror/lang-sql 
                 
                 
                     Build the bundle using the version number from package.json with: 
                     node_modules/.bin/rollup datasette/static/cm-editor-6.0.1.js \
  -f iife \
  -n cm \
  -o datasette/static/cm-editor-6.0.1.bundle.js \
  -p @rollup/plugin-node-resolve \
  -p @rollup/plugin-terser 
                 
                 
                     Update the version reference in the  codemirror.html  template.

references:
[{"href": "https://codemirror.net/", "label": "CodeMirror"}, {"href": "https://latest.datasette.io/fixtures", "label": "this page"}]


id: facets:id1
page: facets
ref: id1
title: Facets
breadcrumbs: []
content:
Datasette facets can be used to add a faceted browse interface to any database table.
            With facets, tables are displayed along with a summary showing the most common values in specified columns.
            These values can be selected to further filter the table. 
         Here's  an example : 
         
         Facets can be specified in two ways: using query string parameters, or in  metadata.json  configuration for the table.

references:
[{"href": "https://congress-legislators.datasettes.com/legislators/legislator_terms?_facet=type&_facet=party&_facet=state&_facet_size=10", "label": "an example"}]


id: facets:facets-in-query-strings
page: facets
ref: facets-in-query-strings
title: Facets in query strings
breadcrumbs: ["Facets"]
references: []
content:
To turn on faceting for specific columns on a Datasette table view, add one or more  _facet=COLUMN  parameters to the URL.
                For example, if you want to turn on facets for the  city_id  and  state  columns, construct a URL that looks like this: 
             /dbname/tablename?_facet=state&_facet=city_id 
             This works for both the HTML interface and the  .json  view.
                When enabled, facets will cause a  facet_results  block to be added to the JSON output, looking something like this: 
             {
  "state": {
    "name": "state",
    "results": [
      {
        "value": "CA",
        "label": "CA",
        "count": 10,
        "toggle_url": "http://...?_facet=city_id&_facet=state&state=CA",
        "selected": false
      },
      {
        "value": "MI",
        "label": "MI",
        "count": 4,
        "toggle_url": "http://...?_facet=city_id&_facet=state&state=MI",
        "selected": false
      },
      {
        "value": "MC",
        "label": "MC",
        "count": 1,
        "toggle_url": "http://...?_facet=city_id&_facet=state&state=MC",
        "selected": false
      }
    ],
    "truncated": false
  }
  "city_id": {
    "name": "city_id",
    "results": [
      {
        "value": 1,
        "label": "San Francisco",
        "count": 6,
        "toggle_url": "http://...?_facet=city_id&_facet=state&city_id=1",
        "selected": false
      },
      {
        "value": 2,
        "label": "Los Angeles",
        "count": 4,
        "toggle_url": "http://...?_facet=city_id&_facet=state&city_id=2",
        "selected": false
      },
      {
        "value": 3,
        "label": "Detroit",
        "count": 4,
        "toggle_url": "http://...?_facet=city_id&_facet=state&city_id=3",
        "selected": false
      },
      {
        "value": 4,
        "label": "Memnonia",
        "count": 1,
        "toggle_url": "http://...?_facet=city_id&_facet=state&city_id=4",
        "selected": false
      }
    ],
    "truncated": false
  }
} 
             If Datasette detects that a column is a foreign key, the  "label"  property will be automatically derived from the detected label column on the referenced table. 
             The default number of facet results returned is 30, controlled by the  default_facet_size  setting.
                You can increase this on an individual page by adding  ?_facet_size=100  to the query string, up to a maximum of  max_returned_rows  (which defaults to 1000).


id: facets:facets-metadata
page: facets
ref: facets-metadata
title: Facets in metadata
breadcrumbs: ["Facets"]
references: []
content:
You can turn facets on by default for specific tables by adding them to a  "facets"  key in a Datasette  Metadata  file. 
             Here's an example that turns on faceting by default for the  qLegalStatus  column in the  Street_Tree_List  table in the  sf-trees  database: 
             [[[cog
from metadata_doc import metadata_example
metadata_example(cog, {
  "databases": {
    "sf-trees": {
      "tables": {
        "Street_Tree_List": {
          "facets": ["qLegalStatus"]
        }
      }
    }
  }
}) 
             ]]] 
             [[[end]]] 
             Facets defined in this way will always be shown in the interface and returned in the API, regardless of the  _facet  arguments passed to the view. 
             You can specify  array  or  date  facets in metadata using JSON objects with a single key of  array  or  date  and a value specifying the column, like this: 
             [[[cog
metadata_example(cog, {
  "facets": [
    {"array": "tags"},
    {"date": "created"}
  ]
}) 
             ]]] 
             [[[end]]] 
             You can change the default facet size (the number of results shown for each facet) for a table using  facet_size : 
             [[[cog
metadata_example(cog, {
  "databases": {
    "sf-trees": {
      "tables": {
        "Street_Tree_List": {
          "facets": ["qLegalStatus"],
          "facet_size": 10
        }
      }
    }
  }
}) 
             ]]] 
             [[[end]]]


id: facets:suggested-facets
page: facets
ref: suggested-facets
title: Suggested facets
breadcrumbs: ["Facets"]
references: []
content:
Datasette's table UI will suggest facets for the user to apply, based on the following criteria: 
             For the currently filtered data are there any columns which, if applied as a facet... 
             
                 
                     Will return 30 or less unique options 
                 
                 
                     Will return more than one unique option 
                 
                 
                     Will return less unique options than the total number of filtered rows 
                 
                 
                     And the query used to evaluate this criteria can be completed in under 50ms 
                 
             
             That last point is particularly important: Datasette runs a query for every column that is displayed on a page, which could get expensive - so to avoid slow load times it sets a time limit of just 50ms for each of those queries.
                This means suggested facets are unlikely to appear for tables with millions of records in them.


id: facets:speeding-up-facets-with-indexes
page: facets
ref: speeding-up-facets-with-indexes
title: Speeding up facets with indexes
breadcrumbs: ["Facets"]
content:
The performance of facets can be greatly improved by adding indexes on the columns you wish to facet by.
                Adding indexes can be performed using the  sqlite3  command-line utility. Here's how to add an index on the  state  column in a table called  Food_Trucks : 
             sqlite3 mydatabase.db 
             SQLite version 3.19.3 2017-06-27 16:48:08
Enter ".help" for usage hints.
sqlite> CREATE INDEX Food_Trucks_state ON Food_Trucks("state"); 
             Or using the  sqlite-utils  command-line utility: 
             sqlite-utils create-index mydatabase.db Food_Trucks state

references:
[{"href": "https://sqlite-utils.datasette.io/en/stable/cli.html#creating-indexes", "label": "sqlite-utils"}]


id: facets:id2
page: facets
ref: id2
title: Facet by JSON array
breadcrumbs: ["Facets"]
content:
If your SQLite installation provides the  json1  extension (you can check using  /-/versions ) Datasette will automatically detect columns that contain JSON arrays of values and offer a faceting interface against those columns. 
             This is useful for modelling things like tags without needing to break them out into a new table. 
             Example here:  latest.datasette.io/fixtures/facetable?_facet_array=tags

references:
[{"href": "https://latest.datasette.io/fixtures/facetable?_facet_array=tags", "label": "latest.datasette.io/fixtures/facetable?_facet_array=tags"}]


id: facets:id3
page: facets
ref: id3
title: Facet by date
breadcrumbs: ["Facets"]
content:
If Datasette finds any columns that contain dates in the first 100 values, it will offer a faceting interface against the dates of those values.
                This works especially well against timestamp values such as  2019-03-01 12:44:00 . 
             Example here:  latest.datasette.io/fixtures/facetable?_facet_date=created

references:
[{"href": "https://latest.datasette.io/fixtures/facetable?_facet_date=created", "label": "latest.datasette.io/fixtures/facetable?_facet_date=created"}]


id: events:id1
page: events
ref: id1
title: Events
breadcrumbs: []
references: []
content:
Datasette includes a mechanism for tracking events that occur while the software is running. This is primarily intended to be used by plugins, which can both trigger events and listen for events. 
         The core Datasette application triggers events when certain things happen. This page describes those events. 
         Plugins can listen for events using the  track_event(datasette, event)  plugin hook, which will be called with instances of the following classes - or additional classes  registered by other plugins . 
         
         
         
         
             class   datasette.events. LoginEvent actor :   dict   |   None 
             
                 Event name:  login 
                 A user (represented by  event.actor ) has logged in. 
             
         
         
         
             class   datasette.events. LogoutEvent actor :   dict   |   None 
             
                 Event name:  logout 
                 A user (represented by  event.actor ) has logged out. 
             
         
         
         
             class   datasette.events. CreateTokenEvent actor :   dict   |   None expires_after :   int   |   None restrict_all :   list restrict_database :   dict restrict_resource :   dict 
             
                 Event name:  create-token 
                 A user created an API token. 
                 
                     
                         Variables 
                         
                             
                                 
                                     expires_after  -- Number of seconds after which this token will expire. 
                                 
                                 
                                     restrict_all  -- Restricted permissions for this token. 
                                 
                                 
                                     restrict_database  -- Restricted database permissions for this token. 
                                 
                                 
                                     restrict_resource  -- Restricted resource permissions for this token. 
                                 
                             
                         
                     
                 
             
         
         
         
             class   datasette.events. CreateTableEvent actor :   dict   |   None database :   str table :   str schema :   str 
             
                 Event name:  create-table 
                 A new table has been created in the database. 
                 
                     
                         Variables 
                         
                             
                                 
                                     database  -- The name of the database where the table was created. 
                                 
                                 
                                     table  -- The name of the table that was created 
                                 
                                 
                                     schema  -- The SQL schema definition for the new table. 
                                 
                             
                         
                     
                 
             
         
         
         
             class   datasette.events. DropTableEvent actor :   dict   |   None database :   str table :   str 
             
                 Event name:  drop-table 
                 A table has been dropped from the database. 
                 
                     
                         Variables 
                         
                             
                                 
                                     database  -- The name of the database where the table was dropped. 
                                 
                                 
                                     table  -- The name of the table that was dropped 
                                 
                             
                         
                     
                 
             
         
         
         
             class   datasette.events. AlterTableEvent actor :   dict   |   None database :   str table :   str before_schema :   str after_schema :   str 
             
                 Event name:  alter-table 
                 A table has been altered. 
                 
                     
                         Variables 
                         
                             
                                 
                                     database  -- The name of the database where the table was altered 
                                 
                                 
                                     table  -- The name of the table that was altered 
                                 
                                 
                                     before_schema  -- The table's SQL schema before the alteration 
                                 
                                 
                                     after_schema  -- The table's SQL schema after the alteration 
                                 
                             
                         
                     
                 
             
         
         
         
             class   datasette.events. InsertRowsEvent actor :   dict   |   None database :   str table :   str num_rows :   int ignore :   bool replace :   bool 
             
                 Event name:  insert-rows 
                 Rows were inserted into a table. 
                 
                     
                         Variables 
                         
                             
                                 
                                     database  -- The name of the database where the rows were inserted. 
                                 
                                 
                                     table  -- The name of the table where the rows were inserted. 
                                 
                                 
                                     num_rows  -- The number of rows that were requested to be inserted. 
                                 
                                 
                                     ignore  -- Was ignore set? 
                                 
                                 
                                     replace  -- Was replace set? 
                                 
                             
                         
                     
                 
             
         
         
         
             class   datasette.events. UpsertRowsEvent actor :   dict   |   None database :   str table :   str num_rows :   int 
             
                 Event name:  upsert-rows 
                 Rows were upserted into a table. 
                 
                     
                         Variables 
                         
                             
                                 
                                     database  -- The name of the database where the rows were inserted. 
                                 
                                 
                                     table  -- The name of the table where the rows were inserted. 
                                 
                                 
                                     num_rows  -- The number of rows that were requested to be inserted. 
                                 
                             
                         
                     
                 
             
         
         
         
             class   datasette.events. UpdateRowEvent actor :   dict   |   None database :   str table :   str pks :   list 
             
                 Event name:  update-row 
                 A row was updated in a table. 
                 
                     
                         Variables 
                         
                             
                                 
                                     database  -- The name of the database where the row was updated. 
                                 
                                 
                                     table  -- The name of the table where the row was updated. 
                                 
                                 
                                     pks  -- The primary key values of the updated row. 
                                 
                             
                         
                     
                 
             
         
         
         
             class   datasette.events. DeleteRowEvent actor :   dict   |   None database :   str table :   str pks :   list 
             
                 Event name:  delete-row 
                 A row was deleted from a table. 
                 
                     
                         Variables 
                         
                             
                                 
                                     database  -- The name of the database where the row was deleted. 
                                 
                                 
                                     table  -- The name of the table where the row was deleted. 
                                 
                                 
                                     pks  -- The primary key values of the deleted row.


id: json_api:id1
page: json_api
ref: id1
title: JSON API
breadcrumbs: []
references: []
content:
Datasette provides a JSON API for your SQLite databases. Anything you can do
            through the Datasette user interface can also be accessed as JSON via the API. 
         To access the API for a page, either click on the  .json  link on that page or
            edit the URL and add a  .json  extension to it.


id: json_api:json-api-default
page: json_api
ref: json-api-default
title: Default representation
breadcrumbs: ["JSON API"]
references: []
content:
The default JSON representation of data from a SQLite table or custom query
                looks like this: 
             {
  "ok": true,
  "rows": [
    {
      "id": 3,
      "name": "Detroit"
    },
    {
      "id": 2,
      "name": "Los Angeles"
    },
    {
      "id": 4,
      "name": "Memnonia"
    },
    {
      "id": 1,
      "name": "San Francisco"
    }
  ],
  "truncated": false
} 
             "ok"  is always  true  if an error did not occur. 
             The  "rows"  key is a list of objects, each one representing a row. 
             The  "truncated"  key lets you know if the query was truncated. This can happen if a SQL query returns more than 1,000 results (or the  max_returned_rows  setting). 
             For table pages, an additional key  "next"  may be present. This indicates that the next page in the pagination set can be retrieved using  ?_next=VALUE .


id: json_api:json-api-shapes
page: json_api
ref: json-api-shapes
title: Different shapes
breadcrumbs: ["JSON API"]
references: []
content:
The  _shape  parameter can be used to access alternative formats for the
                 rows  key which may be more convenient for your application. There are three
                options: 
             
                 
                     ?_shape=objects  -  "rows"  is a list of JSON key/value objects - the default 
                 
                 
                     ?_shape=arrays  -  "rows"  is a list of lists, where the order of values in each list matches the order of the columns 
                 
                 
                     ?_shape=array  - a JSON array of objects - effectively just the  "rows"  key from the default representation 
                 
                 
                     ?_shape=array&_nl=on  - a newline-separated list of JSON objects 
                 
                 
                     ?_shape=arrayfirst  - a flat JSON array containing just the first value from each row 
                 
                 
                     ?_shape=object  - a JSON object keyed using the primary keys of the rows 
                 
             
             _shape=arrays  looks like this: 
             {
  "ok": true,
  "next": null,
  "rows": [
    [3, "Detroit"],
    [2, "Los Angeles"],
    [4, "Memnonia"],
    [1, "San Francisco"]
  ]
} 
             _shape=array  looks like this: 
             [
  {
    "id": 3,
    "name": "Detroit"
  },
  {
    "id": 2,
    "name": "Los Angeles"
  },
  {
    "id": 4,
    "name": "Memnonia"
  },
  {
    "id": 1,
    "name": "San Francisco"
  }
] 
             _shape=array&_nl=on  looks like this: 
             {"id": 1, "value": "Myoporum laetum :: Myoporum"}
{"id": 2, "value": "Metrosideros excelsa :: New Zealand Xmas Tree"}
{"id": 3, "value": "Pinus radiata :: Monterey Pine"} 
             _shape=arrayfirst  looks like this: 
             [1, 2, 3] 
             _shape=object  looks like this: 
             {
  "1": {
    "id": 1,
    "value": "Myoporum laetum :: Myoporum"
  },
  "2": {
    "id": 2,
    "value": "Metrosideros excelsa :: New Zealand Xmas Tree"
  },
  "3": {
    "id": 3,
    "value": "Pinus radiata :: Monterey Pine"
  }
] 
             The  object  shape is only available for queries against tables - custom SQL
                queries and views do not have an obvious primary key so cannot be returned using
                this format. 
             The  object  keys are always strings. If your table has a compound primary
                key, the  object  keys will be a comma-separated string.


id: json_api:json-api-pagination
page: json_api
ref: json-api-pagination
title: Pagination
breadcrumbs: ["JSON API"]
references: [{"href": "https://requests.readthedocs.io/", "label": "requests"}]
content:
The default JSON representation includes a  "next_url"  key which can be used to access the next page of results. If that key is null or missing then it means you have reached the final page of results. 
             Other representations include pagination information in the  link  HTTP header. That header will look something like this: 
             link: <https://latest.datasette.io/fixtures/sortable.json?_next=d%2Cv>; rel="next" 
             Here is an example Python function built using  requests  that returns a list of all of the paginated items from one of these API endpoints: 
             def paginate(url):
    items = []
    while url:
        response = requests.get(url)
        try:
            url = response.links.get("next").get("url")
        except AttributeError:
            url = None
        items.extend(response.json())
    return items


id: json_api:json-api-special
page: json_api
ref: json-api-special
title: Special JSON arguments
breadcrumbs: ["JSON API"]
content:
Every Datasette endpoint that can return JSON also accepts the following
                query string arguments: 
             
                 
                     ?_shape=SHAPE 
                     
                         The shape of the JSON to return, documented above. 
                     
                 
                 
                     ?_nl=on 
                     
                         When used with  ?_shape=array  produces newline-delimited JSON objects. 
                     
                 
                 
                     ?_json=COLUMN1&_json=COLUMN2 
                     
                         If any of your SQLite columns contain JSON values, you can use one or more
                             _json=  parameters to request that those columns be returned as regular
                            JSON. Without this argument those columns will be returned as JSON objects
                            that have been double-encoded into a JSON string value. 
                         Compare  this query without the argument  to  this query using the argument 
                     
                 
                 
                     ?_json_infinity=on 
                     
                         If your data contains infinity or -infinity values, Datasette will replace
                            them with None when returning them as JSON. If you pass  _json_infinity=1 
                            Datasette will instead return them as  Infinity  or  -Infinity  which is
                            invalid JSON but can be processed by some custom JSON parsers. 
                     
                 
                 
                     ?_timelimit=MS 
                     
                         Sets a custom time limit for the query in ms. You can use this for optimistic
                            queries where you would like Datasette to give up if the query takes too
                            long, for example if you want to implement autocomplete search but only if
                            it can be executed in less than 10ms. 
                     
                 
                 
                     ?_ttl=SECONDS 
                     
                         For how many seconds should this response be cached by HTTP proxies? Use
                             ?_ttl=0  to disable HTTP caching entirely for this request. 
                     
                 
                 
                     ?_trace=1 
                     
                         Turns on tracing for this page: SQL queries executed during the request will
                            be gathered and included in the response, either in a new  "_traces"  key
                            for JSON responses or at the bottom of the page if the response is in HTML. 
                         The structure of the data returned here should be considered highly unstable
                            and very likely to change. 
                         Only available if the  trace_debug  setting is enabled.

references:
[{"href": "https://fivethirtyeight.datasettes.com/fivethirtyeight.json?sql=select+%27{%22this+is%22%3A+%22a+json+object%22}%27+as+d&_shape=array", "label": "this query without the argument"}, {"href": "https://fivethirtyeight.datasettes.com/fivethirtyeight.json?sql=select+%27{%22this+is%22%3A+%22a+json+object%22}%27+as+d&_shape=array&_json=d", "label": "this query using the argument"}]


id: json_api:column-filter-arguments
page: json_api
ref: column-filter-arguments
title: Column filter arguments
breadcrumbs: ["JSON API", "Table arguments"]
references: []
content:
You can filter the data returned by the table based on column values using a query string argument. 
                 
                     
                         ?column__exact=value  or  ?_column=value 
                         
                             Returns rows where the specified column exactly matches the value. 
                         
                     
                     
                         ?column__not=value 
                         
                             Returns rows where the column does not match the value. 
                         
                     
                     
                         ?column__contains=value 
                         
                             Rows where the string column contains the specified value ( column like "%value%"  in SQL). 
                         
                     
                     
                         ?column__notcontains=value 
                         
                             Rows where the string column does not contain the specified value ( column not like "%value%"  in SQL). 
                         
                     
                     
                         ?column__endswith=value 
                         
                             Rows where the string column ends with the specified value ( column like "%value"  in SQL). 
                         
                     
                     
                         ?column__startswith=value 
                         
                             Rows where the string column starts with the specified value ( column like "value%"  in SQL). 
                         
                     
                     
                         ?column__gt=value 
                         
                             Rows which are greater than the specified value. 
                         
                     
                     
                         ?column__gte=value 
                         
                             Rows which are greater than or equal to the specified value. 
                         
                     
                     
                         ?column__lt=value 
                         
                             Rows which are less than the specified value. 
                         
                     
                     
                         ?column__lte=value 
                         
                             Rows which are less than or equal to the specified value. 
                         
                     
                     
                         ?column__like=value 
                         
                             Match rows with a LIKE clause, case insensitive and with  %  as the wildcard character. 
                         
                     
                     
                         ?column__notlike=value 
                         
                             Match rows that do not match the provided LIKE clause. 
                         
                     
                     
                         ?column__glob=value 
                         
                             Similar to LIKE but uses Unix wildcard syntax and is case sensitive. 
                         
                     
                     
                         ?column__in=value1,value2,value3 
                         
                             Rows where column matches any of the provided values. 
                             You can use a comma separated string, or you can use a JSON array. 
                             The JSON array option is useful if one of your matching values itself contains a comma: 
                             ?column__in=["value","value,with,commas"] 
                         
                     
                     
                         ?column__notin=value1,value2,value3 
                         
                             Rows where column does not match any of the provided values. The inverse of  __in= . Also supports JSON arrays. 
                         
                     
                     
                         ?column__arraycontains=value 
                         
                             Works against columns that contain JSON arrays - matches if any of the values in that array match the provided value. 
                             This is only available if the  json1  SQLite extension is enabled. 
                         
                     
                     
                         ?column__arraynotcontains=value 
                         
                             Works against columns that contain JSON arrays - matches if none of the values in that array match the provided value. 
                             This is only available if the  json1  SQLite extension is enabled. 
                         
                     
                     
                         ?column__date=value 
                         
                             Column is a datestamp occurring on the specified YYYY-MM-DD date, e.g.  2018-01-02 . 
                         
                     
                     
                         ?column__isnull=1 
                         
                             Matches rows where the column is null. 
                         
                     
                     
                         ?column__notnull=1 
                         
                             Matches rows where the column is not null. 
                         
                     
                     
                         ?column__isblank=1 
                         
                             Matches rows where the column is blank, meaning null or the empty string. 
                         
                     
                     
                         ?column__notblank=1 
                         
                             Matches rows where the column is not blank.


id: json_api:json-api-table-arguments
page: json_api
ref: json-api-table-arguments
title: Special table arguments
breadcrumbs: ["JSON API", "Table arguments"]
content:
?_col=COLUMN1&_col=COLUMN2 
                         
                             List specific columns to display. These will be shown along with any primary keys. 
                         
                     
                     
                         ?_nocol=COLUMN1&_nocol=COLUMN2 
                         
                             List specific columns to hide - any column not listed will be displayed. Primary keys cannot be hidden. 
                         
                     
                     
                         ?_labels=on/off 
                         
                             Expand foreign key references for every possible column. See below. 
                         
                     
                     
                         ?_label=COLUMN1&_label=COLUMN2 
                         
                             Expand foreign key references for one or more specified columns. 
                         
                     
                     
                         ?_size=1000  or  ?_size=max 
                         
                             Sets a custom page size. This cannot exceed the  max_returned_rows  limit
                                passed to  datasette serve . Use  max  to get  max_returned_rows . 
                         
                     
                     
                         ?_sort=COLUMN 
                         
                             Sorts the results by the specified column. 
                         
                     
                     
                         ?_sort_desc=COLUMN 
                         
                             Sorts the results by the specified column in descending order. 
                         
                     
                     
                         ?_search=keywords 
                         
                             For SQLite tables that have been configured for
                                 full-text search  executes a search
                                with the provided keywords. 
                         
                     
                     
                         ?_search_COLUMN=keywords 
                         
                             Like  _search=  but allows you to specify the column to be searched, as
                                opposed to searching all columns that have been indexed by FTS. 
                         
                     
                     
                         ?_searchmode=raw 
                         
                             With this option, queries passed to  ?_search=  or  ?_search_COLUMN=  will
                                not have special characters escaped. This means you can make use of the full
                                set of  advanced SQLite FTS syntax ,
                                though this could potentially result in errors if the wrong syntax is used. 
                         
                     
                     
                         ?_where=SQL-fragment 
                         
                             If the  execute-sql  permission is enabled, this parameter
                                can be used to pass one or more additional SQL fragments to be used in the
                                 WHERE  clause of the SQL used to query the table. 
                             This is particularly useful if you are building a JavaScript application
                                that needs to do something creative but still wants the other conveniences
                                provided by the table view (such as faceting) and hence would like not to
                                have to construct a completely custom SQL query. 
                             Some examples: 
                             
                                 
                                     facetable?_where=_neighborhood like "%c%"&_where=_city_id=3 
                                 
                                 
                                     facetable?_where=_city_id in (select id from facet_cities where name != "Detroit") 
                                 
                             
                         
                     
                     
                         ?_through={json} 
                         
                             This can be used to filter rows via a join against another table. 
                             The JSON parameter must include three keys:  table ,  column  and  value . 
                             table  must be a table that the current table is related to via a foreign key relationship. 
                             column  must be a column in that other table. 
                             value  is the value that you want to match against. 
                             For example, to filter  roadside_attractions  to just show the attractions that have a characteristic of "museum", you would construct this JSON: 
                             {
    "table": "roadside_attraction_characteristics",
    "column": "characteristic_id",
    "value": "1"
} 
                             As a URL, that looks like this: 
                             ?_through={%22table%22:%22roadside_attraction_characteristics%22,%22column%22:%22characteristic_id%22,%22value%22:%221%22} 
                             Here's  an example . 
                         
                     
                     
                         ?_next=TOKEN 
                         
                             Pagination by continuation token - pass the token that was returned in the
                                 "next"  property by the previous page. 
                         
                     
                     
                         ?_facet=column 
                         
                             Facet by column. Can be applied multiple times, see  Facets . Only works on the default JSON output, not on any of the custom shapes. 
                         
                     
                     
                         ?_facet_size=100 
                         
                             Increase the number of facet results returned for each facet. Use  ?_facet_size=max  for the maximum available size, determined by  max_returned_rows . 
                         
                     
                     
                         ?_nofacet=1 
                         
                             Disable all facets and facet suggestions for this page, including any defined by  Facets in metadata . 
                         
                     
                     
                         ?_nosuggest=1 
                         
                             Disable facet suggestions for this page. 
                         
                     
                     
                         ?_nocount=1 
                         
                             Disable the  select count(*)  query used on this page - a count of  None  will be returned instead.

references:
[{"href": "https://www.sqlite.org/fts3.html", "label": "full-text search"}, {"href": "https://www.sqlite.org/fts5.html#full_text_query_syntax", "label": "advanced SQLite FTS syntax"}, {"href": "https://latest.datasette.io/fixtures/facetable?_where=_neighborhood%20like%20%22%c%%22&_where=_city_id=3", "label": "facetable?_where=_neighborhood like \"%c%\"&_where=_city_id=3"}, {"href": "https://latest.datasette.io/fixtures/facetable?_where=_city_id%20in%20(select%20id%20from%20facet_cities%20where%20name%20!=%20%22Detroit%22)", "label": "facetable?_where=_city_id in (select id from facet_cities where name != \"Detroit\")"}, {"href": "https://latest.datasette.io/fixtures/roadside_attractions?_through={%22table%22:%22roadside_attraction_characteristics%22,%22column%22:%22characteristic_id%22,%22value%22:%221%22}", "label": "an example"}]


id: json_api:expand-foreign-keys
page: json_api
ref: expand-foreign-keys
title: Expanding foreign key references
breadcrumbs: ["JSON API"]
references: []
content:
Datasette can detect foreign key relationships and resolve those references into
                labels. The HTML interface does this by default for every detected foreign key
                column - you can turn that off using  ?_labels=off . 
             You can request foreign keys be expanded in JSON using the  _labels=on  or
                 _label=COLUMN  special query string parameters. Here's what an expanded row
                looks like: 
             [
    {
        "rowid": 1,
        "TreeID": 141565,
        "qLegalStatus": {
            "value": 1,
            "label": "Permitted Site"
        },
        "qSpecies": {
            "value": 1,
            "label": "Myoporum laetum :: Myoporum"
        },
        "qAddress": "501X Baker St",
        "SiteOrder": 1
    }
] 
             The column in the foreign key table that is used for the label can be specified
                in  metadata.json  - see  Specifying the label column for a table .


id: json_api:json-api-discover-alternate
page: json_api
ref: json-api-discover-alternate
title: Discovering the JSON for a page
breadcrumbs: ["JSON API"]
references: []
content:
Most of the HTML pages served by Datasette provide a mechanism for discovering their JSON equivalents using the HTML  link  mechanism. 
             You can find this near the top of the source code of those pages, looking like this: 
             <link rel="alternate"
  type="application/json+datasette"
  href="https://latest.datasette.io/fixtures/sortable.json"> 
             The JSON URL is also made available in a  Link  HTTP header for the page: 
             Link: https://latest.datasette.io/fixtures/sortable.json; rel="alternate"; type="application/json+datasette"


id: json_api:json-api-cors
page: json_api
ref: json-api-cors
title: Enabling CORS
breadcrumbs: ["JSON API"]
references: []
content:
If you start Datasette with the  --cors  option, each JSON endpoint will be
                served with the following additional HTTP headers: 
             [[[cog
from datasette.utils import add_cors_headers
import textwrap
headers = {}
add_cors_headers(headers)
output = "\n".join("{}: {}".format(k, v) for k, v in headers.items())
cog.out("\n::\n\n")
cog.out(textwrap.indent(output, '    '))
cog.out("\n\n") 
             ]]] 
             Access-Control-Allow-Origin: *
Access-Control-Allow-Headers: Authorization, Content-Type
Access-Control-Expose-Headers: Link
Access-Control-Allow-Methods: GET, POST, HEAD, OPTIONS
Access-Control-Max-Age: 3600 
             [[[end]]] 
             This allows JavaScript running on any domain to make cross-origin
                requests to interact with the Datasette API. 
             If you start Datasette without the  --cors  option only JavaScript running on
                the same domain as Datasette will be able to access the API. 
             Here's how to serve  data.db  with CORS enabled: 
             datasette data.db --cors


id: json_api:json-api-write
page: json_api
ref: json-api-write
title: The JSON write API
breadcrumbs: ["JSON API"]
references: []
content:
Datasette provides a write API for JSON data. This is a POST-only API that requires an authenticated API token, see  API Tokens . The token will need to have the specified  Permissions .


id: json_api:tableinsertview
page: json_api
ref: tableinsertview
title: Inserting rows
breadcrumbs: ["JSON API", "The JSON write API"]
references: []
content:
This requires the  insert-row  permission. 
                 A single row can be inserted using the  "row"  key: 
                 POST /<database>/<table>/-/insert
Content-Type: application/json
Authorization: Bearer dstok_<rest-of-token> 
                 {
    "row": {
        "column1": "value1",
        "column2": "value2"
    }
} 
                 If successful, this will return a  201  status code and the newly inserted row, for example: 
                 {
    "rows": [
        {
            "id": 1,
            "column1": "value1",
            "column2": "value2"
        }
    ]
} 
                 To insert multiple rows at a time, use the same API method but send a list of dictionaries as the  "rows"  key: 
                 POST /<database>/<table>/-/insert
Content-Type: application/json
Authorization: Bearer dstok_<rest-of-token> 
                 {
    "rows": [
        {
            "column1": "value1",
            "column2": "value2"
        },
        {
            "column1": "value3",
            "column2": "value4"
        }
    ]
} 
                 If successful, this will return a  201  status code and a  {"ok": true}  response body. 
                 The maximum number rows that can be submitted at once defaults to 100, but this can be changed using the  max_insert_rows  setting. 
                 To return the newly inserted rows, add the  "return": true  key to the request body: 
                 {
    "rows": [
        {
            "column1": "value1",
            "column2": "value2"
        },
        {
            "column1": "value3",
            "column2": "value4"
        }
    ],
    "return": true
} 
                 This will return the same  "rows"  key as the single row example above. There is a small performance penalty for using this option. 
                 If any of your rows have a primary key that is already in use, you will get an error and none of the rows will be inserted: 
                 {
    "ok": false,
    "errors": [
        "UNIQUE constraint failed: new_table.id"
    ]
} 
                 Pass  "ignore": true  to ignore these errors and insert the other rows: 
                 {
    "rows": [
        {
            "id": 1,
            "column1": "value1",
            "column2": "value2"
        },
        {
            "id": 2,
            "column1": "value3",
            "column2": "value4"
        }
    ],
    "ignore": true
} 
                 Or you can pass  "replace": true  to replace any rows with conflicting primary keys with the new values. This requires the  update-row  permission. 
                 Pass  "alter: true  to automatically add any missing columns to the table. This requires the  alter-table  permission.


id: json_api:tableupsertview
page: json_api
ref: tableupsertview
title: Upserting rows
breadcrumbs: ["JSON API", "The JSON write API"]
references: []
content:
An upsert is an insert or update operation. If a row with a matching primary key already exists it will be updated - otherwise a new row will be inserted. 
                 The upsert API is mostly the same shape as the  insert API . It requires both the  insert-row  and  update-row  permissions. 
                 POST /<database>/<table>/-/upsert
Content-Type: application/json
Authorization: Bearer dstok_<rest-of-token> 
                 {
    "rows": [
        {
            "id": 1,
            "title": "Updated title for 1",
            "description": "Updated description for 1"
        },
        {
            "id": 2,
            "description": "Updated description for 2",
        },
        {
            "id": 3,
            "title": "Item 3",
            "description": "Description for 3"
        }
    ]
} 
                 Imagine a table with a primary key of  id  and which already has rows with  id  values of  1  and  2 . 
                 The above example will: 
                 
                     
                         Update the row with  id  of  1  to set both  title  and  description  to the new values 
                     
                     
                         Update the row with  id  of  2  to set  title  to the new value -  description  will be left unchanged 
                     
                     
                         Insert a new row with  id  of  3  and both  title  and  description  set to the new values 
                     
                 
                 Similar to  /-/insert , a  row  key with an object can be used instead of a  rows  array to upsert a single row. 
                 If successful, this will return a  200  status code and a  {"ok": true}  response body. 
                 Add  "return": true  to the request body to return full copies of the affected rows after they have been inserted or updated: 
                 {
    "rows": [
        {
            "id": 1,
            "title": "Updated title for 1",
            "description": "Updated description for 1"
        },
        {
            "id": 2,
            "description": "Updated description for 2",
        },
        {
            "id": 3,
            "title": "Item 3",
            "description": "Description for 3"
        }
    ],
    "return": true
} 
                 This will return the following: 
                 {
    "ok": true,
    "rows": [
        {
            "id": 1,
            "title": "Updated title for 1",
            "description": "Updated description for 1"
        },
        {
            "id": 2,
            "title": "Item 2",
            "description": "Updated description for 2"
        },
        {
            "id": 3,
            "title": "Item 3",
            "description": "Description for 3"
        }
    ]
} 
                 When using upsert you must provide the primary key column (or columns if the table has a compound primary key) for every row, or you will get a  400  error: 
                 {
    "ok": false,
    "errors": [
        "Row 0 is missing primary key column(s): \"id\""
    ]
} 
                 If your table does not have an explicit primary key you should pass the SQLite  rowid  key instead. 
                 Pass  "alter: true  to automatically add any missing columns to the table. This requires the  alter-table  permission.


id: json_api:rowupdateview
page: json_api
ref: rowupdateview
title: Updating a row
breadcrumbs: ["JSON API", "The JSON write API"]
references: []
content:
To update a row, make a  POST  to  /<database>/<table>/<row-pks>/-/update . This requires the  update-row  permission. 
                 POST /<database>/<table>/<row-pks>/-/update
Content-Type: application/json
Authorization: Bearer dstok_<rest-of-token> 
                 {
    "update": {
        "text_column": "New text string",
        "integer_column": 3,
        "float_column": 3.14
    }
} 
                 <row-pks>  here is the  tilde-encoded  primary key value of the row to update - or a comma-separated list of primary key values if the table has a composite primary key. 
                 You only need to pass the columns you want to update. Any other columns will be left unchanged. 
                 If successful, this will return a  200  status code and a  {"ok": true}  response body. 
                 Add  "return": true  to the request body to return the updated row: 
                 {
    "update": {
        "title": "New title"
    },
    "return": true
} 
                 The returned JSON will look like this: 
                 {
    "ok": true,
    "row": {
        "id": 1,
        "title": "New title",
        "other_column": "Will be present here too"
    }
} 
                 Any errors will return  {"errors": ["... descriptive message ..."], "ok": false} , and a  400  status code for a bad input or a  403  status code for an authentication or permission error. 
                 Pass  "alter: true  to automatically add any missing columns to the table. This requires the  alter-table  permission.


id: json_api:rowdeleteview
page: json_api
ref: rowdeleteview
title: Deleting a row
breadcrumbs: ["JSON API", "The JSON write API"]
references: []
content:
To delete a row, make a  POST  to  /<database>/<table>/<row-pks>/-/delete . This requires the  delete-row  permission. 
                 POST /<database>/<table>/<row-pks>/-/delete
Content-Type: application/json
Authorization: Bearer dstok_<rest-of-token> 
                 <row-pks>  here is the  tilde-encoded  primary key value of the row to delete - or a comma-separated list of primary key values if the table has a composite primary key. 
                 If successful, this will return a  200  status code and a  {"ok": true}  response body. 
                 Any errors will return  {"errors": ["... descriptive message ..."], "ok": false} , and a  400  status code for a bad input or a  403  status code for an authentication or permission error.


id: json_api:tablecreateview
page: json_api
ref: tablecreateview
title: Creating a table
breadcrumbs: ["JSON API", "The JSON write API"]
references: []
content:
To create a table, make a  POST  to  /<database>/-/create . This requires the  create-table  permission. 
                 POST /<database>/-/create
Content-Type: application/json
Authorization: Bearer dstok_<rest-of-token> 
                 {
    "table": "name_of_new_table",
    "columns": [
        {
            "name": "id",
            "type": "integer"
        },
        {
            "name": "title",
            "type": "text"
        }
    ],
    "pk": "id"
} 
                 The JSON here describes the table that will be created: 
                 
                     
                         table  is the name of the table to create. This field is required. 
                     
                     
                         columns  is a list of columns to create. Each column is a dictionary with  name  and  type  keys. 
                         
                             
                                 name  is the name of the column. This is required. 
                             
                             
                                 type  is the type of the column. This is optional - if not provided,  text  will be assumed. The valid types are  text ,  integer ,  float  and  blob . 
                             
                         
                     
                     
                         pk  is the primary key for the table. This is optional - if not provided, Datasette will create a SQLite table with a hidden  rowid  column. 
                         If the primary key is an integer column, it will be configured to automatically increment for each new record. 
                         If you set this to  id  without including an  id  column in the list of  columns , Datasette will create an auto-incrementing integer ID column for you. 
                     
                     
                         pks  can be used instead of  pk  to create a compound primary key. It should be a JSON list of column names to use in that primary key. 
                     
                     
                         ignore  can be set to  true  to ignore existing rows by primary key if the table already exists. 
                     
                     
                         replace  can be set to  true  to replace existing rows by primary key if the table already exists. This requires the  update-row  permission. 
                     
                     
                         alter  can be set to  true  if you want to automatically add any missing columns to the table. This requires the  alter-table  permission. 
                     
                 
                 If the table is successfully created this will return a  201  status code and the following response: 
                 {
    "ok": true,
    "database": "data",
    "table": "name_of_new_table",
    "table_url": "http://127.0.0.1:8001/data/name_of_new_table",
    "table_api_url": "http://127.0.0.1:8001/data/name_of_new_table.json",
    "schema": "CREATE TABLE [name_of_new_table] (\n   [id] INTEGER PRIMARY KEY,\n   [title] TEXT\n)"
}


id: json_api:tablecreateview-example
page: json_api
ref: tablecreateview-example
title: Creating a table from example data
breadcrumbs: ["JSON API", "The JSON write API"]
references: []
content:
Instead of specifying  columns  directly you can instead pass a single example  row  or a list of  rows .
                    Datasette will create a table with a schema that matches those rows and insert them for you: 
                 POST /<database>/-/create
Content-Type: application/json
Authorization: Bearer dstok_<rest-of-token> 
                 {
    "table": "creatures",
    "rows": [
        {
            "id": 1,
            "name": "Tarantula"
        },
        {
            "id": 2,
            "name": "Kākāpō"
        }
    ],
    "pk": "id"
} 
                 Doing this requires both the  create-table  and  insert-row  permissions. 
                 The  201  response here will be similar to the  columns  form, but will also include the number of rows that were inserted as  row_count : 
                 {
    "ok": true,
    "database": "data",
    "table": "creatures",
    "table_url": "http://127.0.0.1:8001/data/creatures",
    "table_api_url": "http://127.0.0.1:8001/data/creatures.json",
    "schema": "CREATE TABLE [creatures] (\n   [id] INTEGER PRIMARY KEY,\n   [name] TEXT\n)",
    "row_count": 2
} 
                 You can call the create endpoint multiple times for the same table provided you are specifying the table using the  rows  or  row  option. New rows will be inserted into the table each time. This means you can use this API if you are unsure if the relevant table has been created yet. 
                 If you pass a row to the create endpoint with a primary key that already exists you will get an error that looks like this: 
                 {
    "ok": false,
    "errors": [
        "UNIQUE constraint failed: creatures.id"
    ]
} 
                 You can avoid this error by passing the same  "ignore": true  or  "replace": true  options to the create endpoint as you can to the  insert endpoint . 
                 To use the  "replace": true  option you will also need the  update-row  permission. 
                 Pass  "alter": true  to automatically add any missing columns to the existing table that are present in the rows you are submitting. This requires the  alter-table  permission.


id: json_api:tabledropview
page: json_api
ref: tabledropview
title: Dropping tables
breadcrumbs: ["JSON API", "The JSON write API"]
references: []
content:
To drop a table, make a  POST  to  /<database>/<table>/-/drop . This requires the  drop-table  permission. 
                 POST /<database>/<table>/-/drop
Content-Type: application/json
Authorization: Bearer dstok_<rest-of-token> 
                 Without a POST body this will return a status  200  with a note about how many rows will be deleted: 
                 {
    "ok": true,
    "database": "<database>",
    "table": "<table>",
    "row_count": 5,
    "message": "Pass \"confirm\": true to confirm"
} 
                 If you pass the following POST body: 
                 {
    "confirm": true
} 
                 Then the table will be dropped and a status  200  response of  {"ok": true}  will be returned. 
                 Any errors will return  {"errors": ["... descriptive message ..."], "ok": false} , and a  400  status code for a bad input or a  403  status code for an authentication or permission error.


id: deploying:deploying
page: deploying
ref: deploying
title: Deploying Datasette
breadcrumbs: []
references: []
content:
The quickest way to deploy a Datasette instance on the internet is to use the  datasette publish  command, described in  Publishing data . This can be used to quickly deploy Datasette to a number of hosting providers including Heroku, Google Cloud Run and Vercel. 
         You can deploy Datasette to other hosting providers using the instructions on this page.


id: deploying:deploying-fundamentals
page: deploying
ref: deploying-fundamentals
title: Deployment fundamentals
breadcrumbs: ["Deploying Datasette"]
references: []
content:
Datasette can be deployed as a single  datasette  process that listens on a port. Datasette is not designed to be run as root, so that process should listen on a higher port such as port 8000. 
             If you want to serve Datasette on port 80 (the HTTP default port) or port 443 (for HTTPS) you should run it behind a proxy server, such as nginx, Apache or HAProxy. The proxy server can listen on port 80/443 and forward traffic on to Datasette.


id: deploying:deploying-systemd
page: deploying
ref: deploying-systemd
title: Running Datasette using systemd
breadcrumbs: ["Deploying Datasette"]
references: []
content:
You can run Datasette on Ubuntu or Debian systems using  systemd . 
             First, ensure you have Python 3 and  pip  installed. On Ubuntu you can use  sudo apt-get install python3 python3-pip . 
             You can install Datasette into a virtual environment, or you can install it system-wide. To install system-wide, use  sudo pip3 install datasette . 
             Now create a folder for your Datasette databases, for example using  mkdir /home/ubuntu/datasette-root . 
             You can copy a test database into that folder like so: 
             cd /home/ubuntu/datasette-root
curl -O https://latest.datasette.io/fixtures.db 
             Create a file at  /etc/systemd/system/datasette.service  with the following contents: 
             [Unit]
Description=Datasette
After=network.target

[Service]
Type=simple
User=ubuntu
Environment=DATASETTE_SECRET=
WorkingDirectory=/home/ubuntu/datasette-root
ExecStart=datasette serve . -h 127.0.0.1 -p 8000
Restart=on-failure

[Install]
WantedBy=multi-user.target 
             Add a random value for the  DATASETTE_SECRET  - this will be used to sign Datasette cookies such as the CSRF token cookie. You can generate a suitable value like so: 
             python3 -c 'import secrets; print(secrets.token_hex(32))' 
             This configuration will run Datasette against all database files contained in the  /home/ubuntu/datasette-root  directory. If that directory contains a  metadata.yml  (or  .json ) file or a  templates/  or  plugins/  sub-directory those will automatically be loaded by Datasette - see  Configuration directory mode  for details. 
             You can start the Datasette process running using the following: 
             sudo systemctl daemon-reload
sudo systemctl start datasette.service 
             You will need to restart the Datasette service after making changes to its  metadata.json  configuration or adding a new database file to that directory. You can do that using: 
             sudo systemctl restart datasette.service 
             Once the service has started you can confirm that Datasette is running on port 8000 like so: 
             curl 127.0.0.1:8000/-/versions.json
# Should output JSON showing the installed version 
             Datasette will not be accessible from outside the server because it is listening on  127.0.0.1 . You can expose it by instead listening on  0.0.0.0 , but a better way is to set up a proxy such as  nginx  - see  Running Datasette behind a proxy .


id: deploying:deploying-openrc
page: deploying
ref: deploying-openrc
title: Running Datasette using OpenRC
breadcrumbs: ["Deploying Datasette"]
content:
OpenRC is the service manager on non-systemd Linux distributions like  Alpine Linux  and  Gentoo . 
             Create an init script at  /etc/init.d/datasette  with the following contents: 
             #!/sbin/openrc-run

name="datasette"
command="datasette"
command_args="serve -h 0.0.0.0 /path/to/db.db"
command_background=true
pidfile="/run/${RC_SVCNAME}.pid" 
             You then need to configure the service to run at boot and start it: 
             rc-update add datasette
rc-service datasette start

references:
[{"href": "https://www.alpinelinux.org/", "label": "Alpine Linux"}, {"href": "https://www.gentoo.org/", "label": "Gentoo"}]


id: deploying:deploying-buildpacks
page: deploying
ref: deploying-buildpacks
title: Deploying using buildpacks
breadcrumbs: ["Deploying Datasette"]
content:
Some hosting providers such as  Heroku ,  DigitalOcean App Platform  and  Scalingo  support the  Buildpacks standard  for deploying Python web applications. 
             Deploying Datasette on these platforms requires two files:  requirements.txt  and  Procfile . 
             The  requirements.txt  file lets the platform know which Python packages should be installed. It should contain  datasette  at a minimum, but can also list any Datasette plugins you wish to install - for example: 
             datasette
datasette-vega 
             The  Procfile  lets the hosting platform know how to run the command that serves web traffic. It should look like this: 
             web: datasette . -h 0.0.0.0 -p $PORT --cors 
             The  $PORT  environment variable is provided by the hosting platform.  --cors  enables CORS requests from JavaScript running on other websites to your domain - omit this if you don't want to allow CORS. You can add additional Datasette  Settings  options here too. 
             These two files should be enough to deploy Datasette on any host that supports buildpacks. Datasette will serve any SQLite files that are included in the root directory of the application. 
             If you want to build SQLite files or download them as part of the deployment process you can do so using a  bin/post_compile  file. For example, the following  bin/post_compile  will download an example database that will then be served by Datasette: 
             wget https://fivethirtyeight.datasettes.com/fivethirtyeight.db 
             simonw/buildpack-datasette-demo  is an example GitHub repository showing a Datasette configuration that can be deployed to a buildpack-supporting host.

references:
[{"href": "https://www.heroku.com/", "label": "Heroku"}, {"href": "https://www.digitalocean.com/docs/app-platform/", "label": "DigitalOcean App Platform"}, {"href": "https://scalingo.com/", "label": "Scalingo"}, {"href": "https://buildpacks.io/", "label": "Buildpacks standard"}, {"href": "https://github.com/simonw/buildpack-datasette-demo", "label": "simonw/buildpack-datasette-demo"}]


id: deploying:deploying-proxy
page: deploying
ref: deploying-proxy
title: Running Datasette behind a proxy
breadcrumbs: ["Deploying Datasette"]
references: []
content:
You may wish to run Datasette behind an Apache or nginx proxy, using a path within your existing site. 
             You can use the  base_url  configuration setting to tell Datasette to serve traffic with a specific URL prefix. For example, you could run Datasette like this: 
             datasette my-database.db --setting base_url /my-datasette/ -p 8009 
             This will run Datasette with the following URLs: 
             
                 
                     http://127.0.0.1:8009/my-datasette/  - the Datasette homepage 
                 
                 
                     http://127.0.0.1:8009/my-datasette/my-database  - the page for the  my-database.db  database 
                 
                 
                     http://127.0.0.1:8009/my-datasette/my-database/some_table  - the page for the  some_table  table 
                 
             
             You can now set your nginx or Apache server to proxy the  /my-datasette/  path to this Datasette instance.


id: deploying:nginx-proxy-configuration
page: deploying
ref: nginx-proxy-configuration
title: Nginx proxy configuration
breadcrumbs: ["Deploying Datasette", "Running Datasette behind a proxy"]
references: [{"href": "https://nginx.org/", "label": "nginx"}]
content:
Here is an example of an  nginx  configuration file that will proxy traffic to Datasette: 
                 daemon off;

events {
  worker_connections  1024;
}
http {
  server {
    listen 80;
    location /my-datasette {
      proxy_pass http://127.0.0.1:8009/my-datasette;
      proxy_set_header Host $host;
    }
  }
} 
                 You can also use the  --uds  option to Datasette to listen on a Unix domain socket instead of a port, configuring the nginx upstream proxy like this: 
                 daemon off;
events {
  worker_connections  1024;
}
http {
  server {
    listen 80;
    location /my-datasette {
      proxy_pass http://datasette/my-datasette;
      proxy_set_header Host $host;
    }
  }
  upstream datasette {
    server unix:/tmp/datasette.sock;
  }
} 
                 Then run Datasette with  datasette --uds /tmp/datasette.sock path/to/database.db --setting base_url /my-datasette/ .


id: deploying:apache-proxy-configuration
page: deploying
ref: apache-proxy-configuration
title: Apache proxy configuration
breadcrumbs: ["Deploying Datasette", "Running Datasette behind a proxy"]
content:
For  Apache , you can use the  ProxyPass  directive. First make sure the following lines are uncommented: 
                 LoadModule proxy_module lib/httpd/modules/mod_proxy.so
LoadModule proxy_http_module lib/httpd/modules/mod_proxy_http.so 
                 Then add these directives to proxy traffic: 
                 ProxyPass /my-datasette/ http://127.0.0.1:8009/my-datasette/
ProxyPreserveHost On 
                 A live demo of Datasette running behind Apache using this proxy setup can be seen at  datasette-apache-proxy-demo.datasette.io/prefix/ . The code for that demo can be found in the  demos/apache-proxy  directory. 
                 Using  --uds  you can use Unix domain sockets similar to the nginx example: 
                 ProxyPass /my-datasette/ unix:/tmp/datasette.sock|http://localhost/my-datasette/ 
                 The  ProxyPreserveHost On  directive ensures that the original  Host:  header from the incoming request is passed through to Datasette. Datasette needs this to correctly assemble links to other pages using the  .absolute_url(request, path)  method.

references:
[{"href": "https://httpd.apache.org/", "label": "Apache"}, {"href": "https://datasette-apache-proxy-demo.datasette.io/prefix/", "label": "datasette-apache-proxy-demo.datasette.io/prefix/"}, {"href": "https://github.com/simonw/datasette/tree/main/demos/apache-proxy", "label": "demos/apache-proxy"}, {"href": "https://httpd.apache.org/docs/2.4/mod/mod_proxy.html#proxypreservehost", "label": "ProxyPreserveHost On"}]


id: custom_templates:css-classes-on-the-body
page: custom_templates
ref: css-classes-on-the-body
title: CSS classes on the <body>
breadcrumbs: ["Custom pages and templates"]
references: []
content:
Every default template includes CSS classes in the body designed to support
                custom styling. 
             The index template (the top level page at  / ) gets this: 
             <body class="index"> 
             The database template ( /dbname ) gets this: 
             <body class="db db-dbname"> 
             The custom SQL template ( /dbname?sql=... ) gets this: 
             <body class="query db-dbname"> 
             A canned query template ( /dbname/queryname ) gets this: 
             <body class="query db-dbname query-queryname"> 
             The table template ( /dbname/tablename ) gets: 
             <body class="table db-dbname table-tablename"> 
             The row template ( /dbname/tablename/rowid ) gets: 
             <body class="row db-dbname table-tablename"> 
             The  db-x  and  table-x  classes use the database or table names themselves if
                they are valid CSS identifiers. If they aren't, we strip any invalid
                characters out and append a 6 character md5 digest of the original name, in
                order to ensure that multiple tables which resolve to the same stripped
                character version still have different CSS classes. 
             Some examples: 
             "simple" => "simple"
"MixedCase" => "MixedCase"
"-no-leading-hyphens" => "no-leading-hyphens-65bea6"
"_no-leading-underscores" => "no-leading-underscores-b921bc"
"no spaces" => "no-spaces-7088d7"
"-" => "336d5e"
"no $ characters" => "no--characters-59e024" 
             <td>  and  <th>  elements also get custom CSS classes reflecting the
                database column they are representing, for example: 
             <table>
    <thead>
        <tr>
            <th class="col-id" scope="col">id</th>
            <th class="col-name" scope="col">name</th>
        </tr>
    </thead>
    <tbody>
        <tr>
            <td class="col-id"><a href="...">1</a></td>
            <td class="col-name">SMITH</td>
        </tr>
    </tbody>
</table>


id: custom_templates:customization-static-files
page: custom_templates
ref: customization-static-files
title: Serving static files
breadcrumbs: ["Custom pages and templates"]
references: []
content:
Datasette can serve static files for you, using the  --static  option.
                Consider the following directory structure: 
             metadata.json
static-files/styles.css
static-files/app.js 
             You can start Datasette using  --static assets:static-files/  to serve those
                files from the  /assets/  mount point: 
             datasette --config datasette.yaml --static assets:static-files/ --memory 
             The following URLs will now serve the content from those CSS and JS files: 
             http://localhost:8001/assets/styles.css
http://localhost:8001/assets/app.js 
             You can reference those files from  datasette.yaml  like this, see  custom CSS and JavaScript  for more details: 
             [[[cog
from metadata_doc import config_example
config_example(cog, """
    extra_css_urls:
    - /assets/styles.css
    extra_js_urls:
    - /assets/app.js
""") 
             ]]] 
             [[[end]]]


id: custom_templates:publishing-static-assets
page: custom_templates
ref: publishing-static-assets
title: Publishing static assets
breadcrumbs: ["Custom pages and templates"]
references: []
content:
The  datasette publish  command can be used to publish your static assets,
                using the same syntax as above: 
             datasette publish cloudrun mydb.db --static assets:static-files/ 
             This will upload the contents of the  static-files/  directory as part of the
                deployment, and configure Datasette to correctly serve the assets from  /assets/ .


id: custom_templates:customization-custom-templates
page: custom_templates
ref: customization-custom-templates
title: Custom templates
breadcrumbs: ["Custom pages and templates", "Publishing static assets"]
content:
By default, Datasette uses default templates that ship with the package. 
                 You can over-ride these templates by specifying a custom  --template-dir  like
                    this: 
                 datasette mydb.db --template-dir=mytemplates/ 
                 Datasette will now first look for templates in that directory, and fall back on
                    the defaults if no matches are found. 
                 It is also possible to over-ride templates on a per-database, per-row or per-
                    table basis. 
                 The lookup rules Datasette uses are as follows: 
                 Index page (/):
    index.html

Database page (/mydatabase):
    database-mydatabase.html
    database.html

Custom query page (/mydatabase?sql=...):
    query-mydatabase.html
    query.html

Canned query page (/mydatabase/canned-query):
    query-mydatabase-canned-query.html
    query-mydatabase.html
    query.html

Table page (/mydatabase/mytable):
    table-mydatabase-mytable.html
    table.html

Row page (/mydatabase/mytable/id):
    row-mydatabase-mytable.html
    row.html

Table of rows and columns include on table page:
    _table-table-mydatabase-mytable.html
    _table-mydatabase-mytable.html
    _table.html

Table of rows and columns include on row page:
    _table-row-mydatabase-mytable.html
    _table-mydatabase-mytable.html
    _table.html 
                 If a table name has spaces or other unexpected characters in it, the template
                    filename will follow the same rules as our custom  <body>  CSS classes - for
                    example, a table called "Food Trucks" will attempt to load the following
                    templates: 
                 table-mydatabase-Food-Trucks-399138.html
table.html 
                 You can find out which templates were considered for a specific page by viewing
                    source on that page and looking for an HTML comment at the bottom. The comment
                    will look something like this: 
                 <!-- Templates considered: *query-mydb-tz.html, query-mydb.html, query.html --> 
                 This example is from the canned query page for a query called "tz" in the
                    database called "mydb". The asterisk shows which template was selected - so in
                    this case, Datasette found a template file called  query-mydb-tz.html  and
                    used that - but if that template had not been found, it would have tried for
                     query-mydb.html  or the default  query.html . 
                 It is possible to extend the default templates using Jinja template
                    inheritance. If you want to customize EVERY row template with some additional
                    content you can do so by creating a  row.html  template like this: 
                 {% extends "default:row.html" %}

{% block content %}
<h1>EXTRA HTML AT THE TOP OF THE CONTENT BLOCK</h1>
<p>This line renders the original block:</p>
{{ super() }}
{% endblock %} 
                 Note the  default:row.html  template name, which ensures Jinja will inherit
                    from the default template. 
                 The  _table.html  template is included by both the row and the table pages,
                    and a list of rows. The default  _table.html  template renders them as an
                    HTML template and  can be seen here . 
                 You can provide a custom template that applies to all of your databases and
                    tables, or you can provide custom templates for specific tables using the
                    template naming scheme described above. 
                 If you want to present your data in a format other than an HTML table, you
                    can do so by looping through  display_rows  in your own  _table.html 
                    template. You can use  {{ row["column_name"] }}  to output the raw value
                    of a specific column. 
                 If you want to output the rendered HTML version of a column, including any
                    links to foreign keys, you can use  {{ row.display("column_name") }} . 
                 Here is an example of a custom  _table.html  template: 
                 {% for row in display_rows %}
    <div>
        <h2>{{ row["title"] }}</h2>
        <p>{{ row["description"] }}<lp>
        <p>Category: {{ row.display("category_id") }}</p>
    </div>
{% endfor %}

references:
[{"href": "https://github.com/simonw/datasette/blob/main/datasette/templates/_table.html", "label": "can be seen here"}]


id: custom_templates:id1
page: custom_templates
ref: id1
title: Custom pages
breadcrumbs: ["Custom pages and templates", "Publishing static assets"]
references: []
content:
You can add templated pages to your Datasette instance by creating HTML files in a  pages  directory within your  templates  directory. 
                 For example, to add a custom page that is served at  http://localhost/about  you would create a file in  templates/pages/about.html , then start Datasette like this: 
                 datasette mydb.db --template-dir=templates/ 
                 You can nest directories within pages to create a nested structure. To create a  http://localhost:8001/about/map  page you would create  templates/pages/about/map.html .


id: custom_templates:custom-pages-parameters
page: custom_templates
ref: custom-pages-parameters
title: Path parameters for pages
breadcrumbs: ["Custom pages and templates"]
content:
You can define custom pages that match multiple paths by creating files with  {variable}  definitions in their filenames. 
             For example, to capture any request to a URL matching  /about/* , you would create a template in the following location: 
             templates/pages/about/{slug}.html 
             A hit to  /about/news  would render that template and pass in a variable called  slug  with a value of  "news" . 
             If you use this mechanism don't forget to return a 404 if the referenced content could not be found. You can do this using  {{ raise_404() }}  described below. 
             Templates defined using custom page routes work particularly well with the  sql()  template function from  datasette-template-sql  or the  graphql()  template function from  datasette-graphql .

references:
[{"href": "https://github.com/simonw/datasette-template-sql", "label": "datasette-template-sql"}, {"href": "https://github.com/simonw/datasette-graphql#the-graphql-template-function", "label": "datasette-graphql"}]


id: custom_templates:custom-pages-headers
page: custom_templates
ref: custom-pages-headers
title: Custom headers and status codes
breadcrumbs: ["Custom pages and templates"]
references: []
content:
Custom pages default to being served with a content-type of  text/html; charset=utf-8  and a  200  status code. You can change these by calling a custom function from within your template. 
             For example, to serve a custom page with a  418 I'm a teapot  HTTP status code, create a file in  pages/teapot.html  containing the following: 
             {{ custom_status(418) }}
<html>
<head><title>Teapot</title></head>
<body>
I'm a teapot
</body>
</html> 
             To serve a custom HTTP header, add a  custom_header(name, value)  function call. For example: 
             {{ custom_status(418) }}
{{ custom_header("x-teapot", "I am") }}
<html>
<head><title>Teapot</title></head>
<body>
I'm a teapot
</body>
</html> 
             You can verify this is working using  curl  like this: 
             curl -I 'http://127.0.0.1:8001/teapot'
HTTP/1.1 418
date: Sun, 26 Apr 2020 18:38:30 GMT
server: uvicorn
x-teapot: I am
content-type: text/html; charset=utf-8


id: custom_templates:custom-pages-404
page: custom_templates
ref: custom-pages-404
title: Returning 404s
breadcrumbs: ["Custom pages and templates"]
references: []
content:
To indicate that content could not be found and display the default 404 page you can use the  raise_404(message)  function: 
             {% if not rows %}
    {{ raise_404("Content not found") }}
{% endif %} 
             If you call  raise_404()  the other content in your template will be ignored.


id: custom_templates:custom-pages-redirects
page: custom_templates
ref: custom-pages-redirects
title: Custom redirects
breadcrumbs: ["Custom pages and templates"]
references: []
content:
You can use the  custom_redirect(location)  function to redirect users to another page, for example in a file called  pages/datasette.html : 
             {{ custom_redirect("https://github.com/simonw/datasette") }} 
             Now requests to  http://localhost:8001/datasette  will result in a redirect. 
             These redirects are served with a  302 Found  status code by default. You can send a  301 Moved Permanently  code by passing  301  as the second argument to the function: 
             {{ custom_redirect("https://github.com/simonw/datasette", 301) }}


id: custom_templates:custom-pages-errors
page: custom_templates
ref: custom-pages-errors
title: Custom error pages
breadcrumbs: ["Custom pages and templates", "Custom redirects"]
content:
Datasette returns an error page if an unexpected error occurs, access is forbidden or content cannot be found. 
                 You can customize the response returned for these errors by providing a custom error page template. 
                 Content not found errors use a  404.html  template. Access denied errors use  403.html . Invalid input errors use  400.html . Unexpected errors of other kinds use  500.html . 
                 If a template for the specific error code is not found a template called  error.html  will be used instead. If you do not provide that template Datasette's  default error.html template  will be used. 
                 The error template will be passed the following context: 
                 
                     
                         status  - integer 
                         
                             The integer HTTP status code, e.g. 404, 500, 403, 400. 
                         
                     
                     
                         error  - string 
                         
                             Details of the specific error, usually a full sentence. 
                         
                     
                     
                         title  - string or None 
                         
                             A title for the page representing the class of error. This is often  None  for errors that do not provide a title separate from their  error  message.

references:
[{"href": "https://github.com/simonw/datasette/blob/main/datasette/templates/error.html", "label": "default error.html template"}]


id: spatialite:id1
page: spatialite
ref: id1
title: SpatiaLite
breadcrumbs: []
content:
The  SpatiaLite module  for SQLite adds features for handling geographic and spatial data. For an example of what you can do with it, see the tutorial  Building a location to time zone API with SpatiaLite . 
         To use it with Datasette, you need to install the  mod_spatialite  dynamic library. This can then be loaded into Datasette using the  --load-extension  command-line option. 
         Datasette can look for SpatiaLite in common installation locations if you run it like this: 
         datasette --load-extension=spatialite --setting default_allow_sql off 
         If SpatiaLite is in another location, use the full path to the extension instead: 
         datasette --setting default_allow_sql off \
  --load-extension=/usr/local/lib/mod_spatialite.dylib

references:
[{"href": "https://www.gaia-gis.it/fossil/libspatialite/index", "label": "SpatiaLite module"}, {"href": "https://datasette.io/tutorials/spatialite", "label": "Building a location to time zone API with SpatiaLite"}]


id: spatialite:spatialite-warning
page: spatialite
ref: spatialite-warning
title: Warning
breadcrumbs: ["SpatiaLite"]
content:
The SpatiaLite extension adds  a large number of additional SQL functions , some of which are not be safe for untrusted users to execute: they may cause the Datasette server to crash. 
                 You should not expose a SpatiaLite-enabled Datasette instance to the public internet without taking extra measures to secure it against potentially harmful SQL queries. 
                 The following steps are recommended: 
                 
                     
                         Disable arbitrary SQL queries by untrusted users. See  Controlling the ability to execute arbitrary SQL  for ways to do this. The easiest is to start Datasette with the  datasette --setting default_allow_sql off  option. 
                     
                     
                         Define  Canned queries  with the SQL queries that use SpatiaLite functions that you want people to be able to execute. 
                     
                 
                 The  Datasette SpatiaLite tutorial  includes detailed instructions for running SpatiaLite safely using these techniques

references:
[{"href": "https://www.gaia-gis.it/gaia-sins/spatialite-sql-5.0.1.html", "label": "a large number of additional SQL functions"}, {"href": "https://datasette.io/tutorials/spatialite", "label": "Datasette SpatiaLite tutorial"}]


id: spatialite:installing-spatialite-on-os-x
page: spatialite
ref: installing-spatialite-on-os-x
title: Installing SpatiaLite on OS X
breadcrumbs: ["SpatiaLite", "Installation"]
references: [{"href": "https://brew.sh/", "label": "Homebrew"}]
content:
The easiest way to install SpatiaLite on OS X is to use  Homebrew . 
                 brew update
brew install spatialite-tools 
                 This will install the  spatialite  command-line tool and the  mod_spatialite  dynamic library. 
                 You can now run Datasette like so: 
                 datasette --load-extension=spatialite


id: spatialite:installing-spatialite-on-linux
page: spatialite
ref: installing-spatialite-on-linux
title: Installing SpatiaLite on Linux
breadcrumbs: ["SpatiaLite", "Installation"]
references: []
content:
SpatiaLite is packaged for most Linux distributions. 
                 apt install spatialite-bin libsqlite3-mod-spatialite 
                 Depending on your distribution, you should be able to run Datasette something like this: 
                 datasette --load-extension=/usr/lib/x86_64-linux-gnu/mod_spatialite.so 
                 If you are unsure of the location of the module, try running  locate mod_spatialite  and see what comes back.


id: spatialite:spatial-indexing-latitude-longitude-columns
page: spatialite
ref: spatial-indexing-latitude-longitude-columns
title: Spatial indexing latitude/longitude columns
breadcrumbs: ["SpatiaLite"]
references: []
content:
Here's a recipe for taking a table with existing latitude and longitude columns, adding a SpatiaLite POINT geometry column to that table, populating the new column and then populating a spatial index: 
             import sqlite3

conn = sqlite3.connect("museums.db")
# Lead the spatialite extension:
conn.enable_load_extension(True)
conn.load_extension("/usr/local/lib/mod_spatialite.dylib")
# Initialize spatial metadata for this database:
conn.execute("select InitSpatialMetadata(1)")
# Add a geometry column called point_geom to our museums table:
conn.execute(
    "SELECT AddGeometryColumn('museums', 'point_geom', 4326, 'POINT', 2);"
)
# Now update that geometry column with the lat/lon points
conn.execute(
    """
    UPDATE museums SET
    point_geom = GeomFromText('POINT('||"longitude"||' '||"latitude"||')',4326);
"""
)
# Now add a spatial index to that column
conn.execute(
    'select CreateSpatialIndex("museums", "point_geom");'
)
# If you don't commit your changes will not be persisted:
conn.commit()
conn.close()


id: spatialite:making-use-of-a-spatial-index
page: spatialite
ref: making-use-of-a-spatial-index
title: Making use of a spatial index
breadcrumbs: ["SpatiaLite"]
content:
SpatiaLite spatial indexes are R*Trees. They allow you to run efficient bounding box queries using a sub-select, with a similar pattern to that used for  Searches using custom SQL . 
             In the above example, the resulting index will be called  idx_museums_point_geom . This takes the form of a SQLite virtual table. You can inspect its contents using the following query: 
             select * from idx_museums_point_geom limit 10; 
             Here's a live example:  timezones-api.datasette.io/timezones/idx_timezones_Geometry 
             
                 
                     
                     
                     
                     
                     
                     
                         
                             
                                 pkid 
                             
                             
                                 xmin 
                             
                             
                                 xmax 
                             
                             
                                 ymin 
                             
                             
                                 ymax 
                             
                         
                     
                     
                         
                             
                                 1 
                             
                             
                                 -8.601725578308105 
                             
                             
                                 -2.4930307865142822 
                             
                             
                                 4.162120819091797 
                             
                             
                                 10.74019718170166 
                             
                         
                         
                             
                                 2 
                             
                             
                                 -3.2607860565185547 
                             
                             
                                 1.27329421043396 
                             
                             
                                 4.539252281188965 
                             
                             
                                 11.174856185913086 
                             
                         
                         
                             
                                 3 
                             
                             
                                 32.997581481933594 
                             
                             
                                 47.98238754272461 
                             
                             
                                 3.3974475860595703 
                             
                             
                                 14.894054412841797 
                             
                         
                         
                             
                                 4 
                             
                             
                                 -8.66890811920166 
                             
                             
                                 11.997337341308594 
                             
                             
                                 18.9681453704834 
                             
                             
                                 37.296207427978516 
                             
                         
                         
                             
                                 5 
                             
                             
                                 36.43336486816406 
                             
                             
                                 43.300174713134766 
                             
                             
                                 12.354820251464844 
                             
                             
                                 18.070993423461914 
                             
                         
                     
                 
             
             You can now construct efficient bounding box queries that will make use of the index like this: 
             select * from museums where museums.rowid in (
    SELECT pkid FROM idx_museums_point_geom
    -- left-hand-edge of point > left-hand-edge of bbox (minx)
    where xmin > :bbox_minx
    -- right-hand-edge of point < right-hand-edge of bbox (maxx)
    and xmax < :bbox_maxx
    -- bottom-edge of point > bottom-edge of bbox (miny)
    and ymin > :bbox_miny
    -- top-edge of point < top-edge of bbox (maxy)
    and ymax < :bbox_maxy
); 
             Spatial indexes can be created against polygon columns as well as point columns, in which case they will represent the minimum bounding rectangle of that polygon. This is useful for accelerating  within  queries, as seen in the Timezones API example.

references:
[{"href": "https://timezones-api.datasette.io/timezones/idx_timezones_Geometry", "label": "timezones-api.datasette.io/timezones/idx_timezones_Geometry"}]


id: spatialite:importing-shapefiles-into-spatialite
page: spatialite
ref: importing-shapefiles-into-spatialite
title: Importing shapefiles into SpatiaLite
breadcrumbs: ["SpatiaLite"]
content:
The  shapefile format  is a common format for distributing geospatial data. You can use the  spatialite  command-line tool to create a new database table from a shapefile. 
             Try it now with the North America shapefile available from the University of North Carolina  Global River Database  project. Download the file and unzip it (this will create files called  narivs.dbf ,  narivs.prj ,  narivs.shp  and  narivs.shx  in the current directory), then run the following: 
             spatialite rivers-database.db 
             SpatiaLite version ..: 4.3.0a       Supported Extensions:
...
spatialite> .loadshp narivs rivers CP1252 23032
========
Loading shapefile at 'narivs' into SQLite table 'rivers'
...
Inserted 467973 rows into 'rivers' from SHAPEFILE 
             This will load the data from the  narivs  shapefile into a new database table called  rivers . 
             Exit out of  spatialite  (using  Ctrl+D ) and run Datasette against your new database like this: 
             datasette rivers-database.db \
    --load-extension=/usr/local/lib/mod_spatialite.dylib 
             If you browse to  http://localhost:8001/rivers-database/rivers  you will see the new table... but the  Geometry  column will contain unreadable binary data (SpatiaLite uses  a custom format based on WKB ). 
             The easiest way to turn this into semi-readable data is to use the SpatiaLite  AsGeoJSON  function. Try the following using the SQL query interface at  http://localhost:8001/rivers-database : 
             select *, AsGeoJSON(Geometry) from rivers limit 10; 
             This will give you back an additional column of GeoJSON. You can copy and paste GeoJSON from this column into the debugging tool at  geojson.io  to visualize it on a map. 
             To see a more interesting example, try ordering the records with the longest geometry first. Since there are 467,000 rows in the table you will first need to increase the SQL time limit imposed by Datasette: 
             datasette rivers-database.db \
    --load-extension=/usr/local/lib/mod_spatialite.dylib \
    --setting sql_time_limit_ms 10000 
             Now try the following query: 
             select *, AsGeoJSON(Geometry) from rivers
order by length(Geometry) desc limit 10;

references:
[{"href": "https://en.wikipedia.org/wiki/Shapefile", "label": "shapefile format"}, {"href": "http://gaia.geosci.unc.edu/rivers/", "label": "Global River Database"}, {"href": "https://www.gaia-gis.it/gaia-sins/BLOB-Geometry.html", "label": "a custom format based on WKB"}, {"href": "https://geojson.io/", "label": "geojson.io"}]


id: spatialite:importing-geojson-polygons-using-shapely
page: spatialite
ref: importing-geojson-polygons-using-shapely
title: Importing GeoJSON polygons using Shapely
breadcrumbs: ["SpatiaLite"]
content:
Another common form of polygon data is the GeoJSON format. This can be imported into SpatiaLite directly, or by using the  Shapely  Python library. 
             Who's On First  is an excellent source of openly licensed GeoJSON polygons. Let's import the geographical polygon for Wales. First, we can use the Who's On First Spelunker tool to find the record for Wales: 
             spelunker.whosonfirst.org/id/404227475 
             That page includes a link to the GeoJSON record, which can be accessed here: 
             data.whosonfirst.org/404/227/475/404227475.geojson 
             Here's Python code to create a SQLite database, enable SpatiaLite, create a places table and then add a record for Wales: 
             import sqlite3

conn = sqlite3.connect("places.db")
# Enable SpatialLite extension
conn.enable_load_extension(True)
conn.load_extension("/usr/local/lib/mod_spatialite.dylib")
# Create the masic countries table
conn.execute("select InitSpatialMetadata(1)")
conn.execute(
    "create table places (id integer primary key, name text);"
)
# Add a MULTIPOLYGON Geometry column
conn.execute(
    "SELECT AddGeometryColumn('places', 'geom', 4326, 'MULTIPOLYGON', 2);"
)
# Add a spatial index against the new column
conn.execute("SELECT CreateSpatialIndex('places', 'geom');")
# Now populate the table
from shapely.geometry.multipolygon import MultiPolygon
from shapely.geometry import shape
import requests

geojson = requests.get(
    "https://data.whosonfirst.org/404/227/475/404227475.geojson"
).json()
# Convert to "Well Known Text" format
wkt = shape(geojson["geometry"]).wkt
# Insert and commit the record
conn.execute(
    "INSERT INTO places (id, name, geom) VALUES(null, ?, GeomFromText(?, 4326))",
    ("Wales", wkt),
)
conn.commit()

references:
[{"href": "https://pypi.org/project/Shapely/", "label": "Shapely"}, {"href": "https://whosonfirst.org/", "label": "Who's On First"}, {"href": "https://spelunker.whosonfirst.org/id/404227475/", "label": "spelunker.whosonfirst.org/id/404227475"}, {"href": "https://data.whosonfirst.org/404/227/475/404227475.geojson", "label": "data.whosonfirst.org/404/227/475/404227475.geojson"}]


id: spatialite:querying-polygons-using-within
page: spatialite
ref: querying-polygons-using-within
title: Querying polygons using within()
breadcrumbs: ["SpatiaLite"]
references: []
content:
The  within()  SQL function can be used to check if a point is within a geometry: 
             select
    name
from
    places
where
   within(GeomFromText('POINT(-3.1724366 51.4704448)'), places.geom); 
             The  GeomFromText()  function takes a string of well-known text. Note that the order used here is  longitude  then   latitude . 
             To run that same  within()  query in a way that benefits from the spatial index, use the following: 
             select
    name
from
    places
where
    within(GeomFromText('POINT(-3.1724366 51.4704448)'), places.geom)
    and rowid in (
        SELECT pkid FROM idx_places_geom
        where xmin < -3.1724366
        and xmax > -3.1724366
        and ymin < 51.4704448
        and ymax > 51.4704448
    );


id: metadata:id1
page: metadata
ref: id1
title: Metadata
breadcrumbs: []
references: []
content:
Data loves metadata. Any time you run Datasette you can optionally include a
            YAML or JSON file with metadata about your databases and tables. Datasette will then
            display that information in the web UI. 
         Run Datasette like this: 
         datasette database1.db database2.db --metadata metadata.yaml 
         Your  metadata.yaml  file can look something like this: 
         [[[cog
from metadata_doc import metadata_example
metadata_example(cog, {
    "title": "Custom title for your index page",
    "description": "Some description text can go here",
    "license": "ODbL",
    "license_url": "https://opendatacommons.org/licenses/odbl/",
    "source": "Original Data Source",
    "source_url": "http://example.com/"
}) 
         ]]] 
         [[[end]]] 
         Choosing YAML over JSON adds support for multi-line strings and comments. 
         The above metadata will be displayed on the index page of your Datasette-powered
            site. The source and license information will also be included in the footer of
            every page served by Datasette. 
         Any special HTML characters in  description  will be escaped. If you want to
            include HTML in your description, you can use a  description_html  property
            instead.


id: metadata:per-database-and-per-table-metadata
page: metadata
ref: per-database-and-per-table-metadata
title: Per-database and per-table metadata
breadcrumbs: ["Metadata"]
references: []
content:
Metadata at the top level of the file will be shown on the index page and in the
                footer on every page of the site. The license and source is expected to apply to
                all of your data. 
             You can also provide metadata at the per-database or per-table level, like this: 
             [[[cog
metadata_example(cog, {
    "databases": {
        "database1": {
            "source": "Alternative source",
            "source_url": "http://example.com/",
            "tables": {
                "example_table": {
                    "description_html": "Custom <em>table</em> description",
                    "license": "CC BY 3.0 US",
                    "license_url": "https://creativecommons.org/licenses/by/3.0/us/"
                }
            }
        }
    }
}) 
             ]]] 
             [[[end]]] 
             Each of the top-level metadata fields can be used at the database and table level.


id: metadata:metadata-source-license-about
page: metadata
ref: metadata-source-license-about
title: Source, license and about
breadcrumbs: ["Metadata"]
references: []
content:
The three visible metadata fields you can apply to everything, specific databases or specific tables are source, license and about. All three are optional. 
             source  and  source_url  should be used to indicate where the underlying data came from. 
             license  and  license_url  should be used to indicate the license under which the data can be used. 
             about  and  about_url  can be used to link to further information about the project - an accompanying blog entry for example. 
             For each of these you can provide just the  *_url  field and Datasette will treat that as the default link label text and display the URL directly on the page.


id: metadata:metadata-column-descriptions
page: metadata
ref: metadata-column-descriptions
title: Column descriptions
breadcrumbs: ["Metadata"]
content:
You can include descriptions for your columns by adding a  "columns": {"name-of-column": "description-of-column"}  block to your table metadata: 
             [[[cog
metadata_example(cog, {
    "databases": {
        "database1": {
            "tables": {
                "example_table": {
                    "columns": {
                        "column1": "Description of column 1",
                        "column2": "Description of column 2"
                    }
                }
            }
        }
    }
}) 
             ]]] 
             [[[end]]] 
             These will be displayed at the top of the table page, and will also show in the cog menu for each column. 
             You can see an example of how these look at  latest.datasette.io/fixtures/roadside_attractions .

references:
[{"href": "https://latest.datasette.io/fixtures/roadside_attractions", "label": "latest.datasette.io/fixtures/roadside_attractions"}]


id: metadata:specifying-units-for-a-column
page: metadata
ref: specifying-units-for-a-column
title: Specifying units for a column
breadcrumbs: ["Metadata"]
content:
Datasette supports attaching units to a column, which will be used when displaying
                values from that column. SI prefixes will be used where appropriate. 
             Column units are configured in the metadata like so: 
             [[[cog
metadata_example(cog, {
    "databases": {
        "database1": {
            "tables": {
                "example_table": {
                    "units": {
                        "column1": "metres",
                        "column2": "Hz"
                    }
                }
            }
        }
    }
}) 
             ]]] 
             [[[end]]] 
             Units are interpreted using  Pint , and you can see the full list of available units in
                Pint's  unit registry . You can also add  custom units  to the metadata, which will be
                registered with Pint: 
             [[[cog
metadata_example(cog, {
    "custom_units": [
        "decibel = [] = dB"
    ]
}) 
             ]]] 
             [[[end]]]

references:
[{"href": "https://pint.readthedocs.io/", "label": "Pint"}, {"href": "https://github.com/hgrecco/pint/blob/master/pint/default_en.txt", "label": "unit registry"}, {"href": "http://pint.readthedocs.io/en/latest/defining.html", "label": "custom units"}]


id: metadata:metadata-default-sort
page: metadata
ref: metadata-default-sort
title: Setting a default sort order
breadcrumbs: ["Metadata"]
references: []
content:
By default Datasette tables are sorted by primary key. You can over-ride this default for a specific table using the  "sort"  or  "sort_desc"  metadata properties: 
             [[[cog
metadata_example(cog, {
    "databases": {
        "mydatabase": {
            "tables": {
                "example_table": {
                    "sort": "created"
                }
            }
        }
    }
}) 
             ]]] 
             [[[end]]] 
             Or use  "sort_desc"  to sort in descending order: 
             [[[cog
metadata_example(cog, {
    "databases": {
        "mydatabase": {
            "tables": {
                "example_table": {
                    "sort_desc": "created"
                }
            }
        }
    }
}) 
             ]]] 
             [[[end]]]


id: metadata:metadata-page-size
page: metadata
ref: metadata-page-size
title: Setting a custom page size
breadcrumbs: ["Metadata"]
references: []
content:
Datasette defaults to displaying 100 rows per page, for both tables and views. You can change this default page size on a per-table or per-view basis using the  "size"  key in  metadata.json : 
             [[[cog
metadata_example(cog, {
    "databases": {
        "mydatabase": {
            "tables": {
                "example_table": {
                    "size": 10
                }
            }
        }
    }
}) 
             ]]] 
             [[[end]]] 
             This size can still be over-ridden by passing e.g.  ?_size=50  in the query string.


id: metadata:metadata-sortable-columns
page: metadata
ref: metadata-sortable-columns
title: Setting which columns can be used for sorting
breadcrumbs: ["Metadata"]
references: []
content:
Datasette allows any column to be used for sorting by default. If you need to
                control which columns are available for sorting you can do so using the optional
                 sortable_columns  key: 
             [[[cog
metadata_example(cog, {
    "databases": {
        "database1": {
            "tables": {
                "example_table": {
                    "sortable_columns": [
                        "height",
                        "weight"
                    ]
                }
            }
        }
    }
}) 
             ]]] 
             [[[end]]] 
             This will restrict sorting of  example_table  to just the  height  and
                 weight  columns. 
             You can also disable sorting entirely by setting  "sortable_columns": [] 
             You can use  sortable_columns  to enable specific sort orders for a view called  name_of_view  in the database  my_database  like so: 
             [[[cog
metadata_example(cog, {
    "databases": {
        "my_database": {
            "tables": {
                "name_of_view": {
                    "sortable_columns": [
                        "clicks",
                        "impressions"
                    ]
                }
            }
        }
    }
}) 
             ]]] 
             [[[end]]]


id: metadata:label-columns
page: metadata
ref: label-columns
title: Specifying the label column for a table
breadcrumbs: ["Metadata"]
references: []
content:
Datasette's HTML interface attempts to display foreign key references as
                labelled hyperlinks. By default, it looks for referenced tables that only have
                two columns: a primary key column and one other. It assumes that the second
                column should be used as the link label. 
             If your table has more than two columns you can specify which column should be
                used for the link label with the  label_column  property: 
             [[[cog
metadata_example(cog, {
    "databases": {
        "database1": {
            "tables": {
                "example_table": {
                    "label_column": "title"
                }
            }
        }
    }
}) 
             ]]] 
             [[[end]]]


id: metadata:metadata-hiding-tables
page: metadata
ref: metadata-hiding-tables
title: Hiding tables
breadcrumbs: ["Metadata"]
references: []
content:
You can hide tables from the database listing view (in the same way that FTS and
                SpatiaLite tables are automatically hidden) using  "hidden": true : 
             [[[cog
metadata_example(cog, {
    "databases": {
        "database1": {
            "tables": {
                "example_table": {
                    "hidden": True
                }
            }
        }
    }
}) 
             ]]] 
             [[[end]]]


id: metadata:top-level-metadata
page: metadata
ref: top-level-metadata
title: Top-level metadata
breadcrumbs: ["Metadata", "Metadata reference"]
references: []
content:
"Top-level" metadata refers to fields that can be specified at the root level  of a metadata file. These attributes are meant to describe the entire Datasette instance. 
                 The following are the full list of allowed top-level metadata fields: 
                 
                     
                         title 
                     
                     
                         description 
                     
                     
                         description_html 
                     
                     
                         license 
                     
                     
                         license_url 
                     
                     
                         source 
                     
                     
                         source_url


id: metadata:database-level-metadata
page: metadata
ref: database-level-metadata
title: Database-level metadata
breadcrumbs: ["Metadata", "Metadata reference"]
references: []
content:
"Database-level" metadata refers to fields that can be specified for each database in a Datasette instance. These attributes should be listed under a database inside the  "databases"  field. 
                 The following are the full list of allowed database-level metadata fields: 
                 
                     
                         source 
                     
                     
                         source_url 
                     
                     
                         license 
                     
                     
                         license_url 
                     
                     
                         about 
                     
                     
                         about_url


id: metadata:table-level-metadata
page: metadata
ref: table-level-metadata
title: Table-level metadata
breadcrumbs: ["Metadata", "Metadata reference"]
references: []
content:
"Table-level" metadata refers to fields that can be specified for each table in a Datasette instance. These attributes should be listed under a specific table using the  "tables"  field. 
                 The following are the full list of allowed table-level metadata fields: 
                 
                     
                         source 
                     
                     
                         source_url 
                     
                     
                         license 
                     
                     
                         license_url 
                     
                     
                         about 
                     
                     
                         about_url 
                     
                     
                         hidden 
                     
                     
                         sort/sort_desc 
                     
                     
                         size 
                     
                     
                         sortable_columns 
                     
                     
                         label_column 
                     
                     
                         facets 
                     
                     
                         fts_table 
                     
                     
                         fts_pk 
                     
                     
                         searchmode 
                     
                     
                         columns


id: pages:pages
page: pages
ref: pages
title: Pages and API endpoints
breadcrumbs: []
references: []
content:
The Datasette web application offers a number of different pages that can be accessed to explore the data in question, each of which is accompanied by an equivalent JSON API.


id: pages:indexview
page: pages
ref: indexview
title: Top-level index
breadcrumbs: ["Pages and API endpoints"]
content:
The root page of any Datasette installation is an index page that lists all of the currently attached databases. Some examples: 
             
                 
                     fivethirtyeight.datasettes.com 
                 
                 
                     global-power-plants.datasettes.com 
                 
                 
                     register-of-members-interests.datasettes.com 
                 
             
             Add  /.json  to the end of the URL for the JSON version of the underlying data: 
             
                 
                     fivethirtyeight.datasettes.com/.json 
                 
                 
                     global-power-plants.datasettes.com/.json 
                 
                 
                     register-of-members-interests.datasettes.com/.json

references:
[{"href": "https://fivethirtyeight.datasettes.com/", "label": "fivethirtyeight.datasettes.com"}, {"href": "https://global-power-plants.datasettes.com/", "label": "global-power-plants.datasettes.com"}, {"href": "https://register-of-members-interests.datasettes.com/", "label": "register-of-members-interests.datasettes.com"}, {"href": "https://fivethirtyeight.datasettes.com/.json", "label": "fivethirtyeight.datasettes.com/.json"}, {"href": "https://global-power-plants.datasettes.com/.json", "label": "global-power-plants.datasettes.com/.json"}, {"href": "https://register-of-members-interests.datasettes.com/.json", "label": "register-of-members-interests.datasettes.com/.json"}]


id: pages:databaseview
page: pages
ref: databaseview
title: Database
breadcrumbs: ["Pages and API endpoints"]
content:
Each database has a page listing the tables, views and canned queries available for that database. If the  execute-sql  permission is enabled (it's on by default) there will also be an interface for executing arbitrary SQL select queries against the data. 
             Examples: 
             
                 
                     fivethirtyeight.datasettes.com/fivethirtyeight 
                 
                 
                     global-power-plants.datasettes.com/global-power-plants 
                 
             
             The JSON version of this page provides programmatic access to the underlying data: 
             
                 
                     fivethirtyeight.datasettes.com/fivethirtyeight.json 
                 
                 
                     global-power-plants.datasettes.com/global-power-plants.json

references:
[{"href": "https://fivethirtyeight.datasettes.com/fivethirtyeight", "label": "fivethirtyeight.datasettes.com/fivethirtyeight"}, {"href": "https://global-power-plants.datasettes.com/global-power-plants", "label": "global-power-plants.datasettes.com/global-power-plants"}, {"href": "https://fivethirtyeight.datasettes.com/fivethirtyeight.json", "label": "fivethirtyeight.datasettes.com/fivethirtyeight.json"}, {"href": "https://global-power-plants.datasettes.com/global-power-plants.json", "label": "global-power-plants.datasettes.com/global-power-plants.json"}]


id: pages:databaseview-hidden
page: pages
ref: databaseview-hidden
title: Hidden tables
breadcrumbs: ["Pages and API endpoints", "Database"]
references: []
content:
Some tables listed on the database page are treated as hidden. Hidden tables are not completely invisible - they can be accessed through the "hidden tables" link at the bottom of the page. They are hidden because they represent low-level implementation details which are generally not useful to end-users of Datasette. 
                 The following tables are hidden by default: 
                 
                     
                         Any table with a name that starts with an underscore - this is a Datasette convention to help plugins easily hide their own internal tables. 
                     
                     
                         Tables that have been configured as  "hidden": true  using  Hiding tables . 
                     
                     
                         *_fts  tables that implement SQLite full-text search indexes. 
                     
                     
                         Tables relating to the inner workings of the SpatiaLite SQLite extension. 
                     
                     
                         sqlite_stat  tables used to store statistics used by the query optimizer.


id: pages:tableview
page: pages
ref: tableview
title: Table
breadcrumbs: ["Pages and API endpoints"]
content:
The table page is the heart of Datasette: it allows users to interactively explore the contents of a database table, including sorting, filtering,  Full-text search  and applying  Facets . 
             The HTML interface is worth spending some time exploring. As with other pages, you can return the JSON data by appending  .json  to the URL path, before any  ?  query string arguments. 
             The query string arguments are described in more detail here:  Table arguments 
             You can also use the table page to interactively construct a SQL query - by applying different filters and a sort order for example - and then click the "View and edit SQL" link to see the SQL query that was used for the page and edit and re-submit it. 
             Some examples: 
             
                 
                     ../items  lists all of the line-items registered by UK MPs as potential conflicts of interest. It demonstrates Datasette's support for  Full-text search . 
                 
                 
                     ../antiquities-act%2Factions_under_antiquities_act  is an interface for exploring the "actions under the antiquities act" data table published by FiveThirtyEight. 
                 
                 
                     ../global-power-plants?country_long=United+Kingdom&primary_fuel=Gas  is a filtered table page showing every Gas power plant in the United Kingdom. It includes some default facets (configured using  its metadata.json ) and uses the  datasette-cluster-map  plugin to show a map of the results.

references:
[{"href": "https://register-of-members-interests.datasettes.com/regmem/items", "label": "../items"}, {"href": "https://fivethirtyeight.datasettes.com/fivethirtyeight/antiquities-act%2Factions_under_antiquities_act", "label": "../antiquities-act%2Factions_under_antiquities_act"}, {"href": "https://global-power-plants.datasettes.com/global-power-plants/global-power-plants?_facet=primary_fuel&_facet=owner&_facet=country_long&country_long__exact=United+Kingdom&primary_fuel=Gas", "label": "../global-power-plants?country_long=United+Kingdom&primary_fuel=Gas"}, {"href": "https://global-power-plants.datasettes.com/-/metadata", "label": "its metadata.json"}, {"href": "https://github.com/simonw/datasette-cluster-map", "label": "datasette-cluster-map"}]


id: pages:rowview
page: pages
ref: rowview
title: Row
breadcrumbs: ["Pages and API endpoints"]
content:
Every row in every Datasette table has its own URL. This means individual records can be linked to directly. 
             Table cells with extremely long text contents are truncated on the table view according to the  truncate_cells_html  setting. If a cell has been truncated the full length version of that cell will be available on the row page. 
             Rows which are the targets of foreign key references from other tables will show a link to a filtered search for all records that reference that row. Here's an example from the Registers of Members Interests database: 
             ../people/uk~2Eorg~2Epublicwhip~2Fperson~2F10001 
             Note that this URL includes the encoded primary key of the record. 
             Here's that same page as JSON: 
             ../people/uk~2Eorg~2Epublicwhip~2Fperson~2F10001.json

references:
[{"href": "https://register-of-members-interests.datasettes.com/regmem/people/uk~2Eorg~2Epublicwhip~2Fperson~2F10001", "label": "../people/uk~2Eorg~2Epublicwhip~2Fperson~2F10001"}, {"href": "https://register-of-members-interests.datasettes.com/regmem/people/uk~2Eorg~2Epublicwhip~2Fperson~2F10001.json", "label": "../people/uk~2Eorg~2Epublicwhip~2Fperson~2F10001.json"}]


id: testing_plugins:id1
page: testing_plugins
ref: id1
title: Testing plugins
breadcrumbs: []
content:
We recommend using  pytest  to write automated tests for your plugins. 
         If you use the template described in  Starting an installable plugin using cookiecutter  your plugin will start with a single test in your  tests/  directory that looks like this: 
         from datasette.app import Datasette
import pytest


@pytest.mark.asyncio
async def test_plugin_is_installed():
    datasette = Datasette(memory=True)
    response = await datasette.client.get("/-/plugins.json")
    assert response.status_code == 200
    installed_plugins = {p["name"] for p in response.json()}
    assert (
        "datasette-plugin-template-demo"
        in installed_plugins
    ) 
         This test uses the  datasette.client  object to exercise a test instance of Datasette.  datasette.client  is a wrapper around the  HTTPX  Python library which can imitate HTTP requests using ASGI. This is the recommended way to write tests against a Datasette instance. 
         This test also uses the  pytest-asyncio  package to add support for  async def  test functions running under pytest. 
         You can install these packages like so: 
         pip install pytest pytest-asyncio 
         If you are building an installable package you can add them as test dependencies to your  setup.py  module like this: 
         setup(
    name="datasette-my-plugin",
    # ...
    extras_require={"test": ["pytest", "pytest-asyncio"]},
    tests_require=["datasette-my-plugin[test]"],
) 
         You can then install the test dependencies like so: 
         pip install -e '.[test]' 
         Then run the tests using pytest like so: 
         pytest

references:
[{"href": "https://docs.pytest.org/", "label": "pytest"}, {"href": "https://www.python-httpx.org/", "label": "HTTPX"}, {"href": "https://pypi.org/project/pytest-asyncio/", "label": "pytest-asyncio"}]


id: testing_plugins:testing-plugins-datasette-test-instance
page: testing_plugins
ref: testing-plugins-datasette-test-instance
title: Setting up a Datasette test instance
breadcrumbs: ["Testing plugins"]
references: []
content:
The above example shows the easiest way to start writing tests against a Datasette instance: 
             from datasette.app import Datasette
import pytest


@pytest.mark.asyncio
async def test_plugin_is_installed():
    datasette = Datasette(memory=True)
    response = await datasette.client.get("/-/plugins.json")
    assert response.status_code == 200 
             Creating a  Datasette()  instance like this as useful shortcut in tests, but there is one detail you need to be aware of. It's important to ensure that the async method  .invoke_startup()  is called on that instance. You can do that like this: 
             datasette = Datasette(memory=True)
await datasette.invoke_startup() 
             This method registers any  startup(datasette)  or  prepare_jinja2_environment(env, datasette)  plugins that might themselves need to make async calls. 
             If you are using  await datasette.client.get()  and similar methods then you don't need to worry about this - Datasette automatically calls  invoke_startup()  the first time it handles a request.


id: testing_plugins:testing-datasette-client
page: testing_plugins
ref: testing-datasette-client
title: Using datasette.client in tests
breadcrumbs: ["Testing plugins"]
references: [{"href": "https://www.python-httpx.org/async/", "label": "HTTPX async client"}]
content:
The  datasette.client  mechanism is designed for use in tests. It provides access to a pre-configured  HTTPX async client  instance that can make GET, POST and other HTTP requests against a Datasette instance from inside a test. 
             A simple test looks like this: 
             @pytest.mark.asyncio
async def test_homepage():
    ds = Datasette(memory=True)
    response = await ds.client.get("/")
    html = response.text
    assert "<h1>" in html
 
             Or for a JSON API: 
             @pytest.mark.asyncio
async def test_actor_is_null():
    ds = Datasette(memory=True)
    response = await ds.client.get("/-/actor.json")
    assert response.json() == {"actor": None}
 
             To make requests as an authenticated actor, create a signed  ds_cookie  using the  datasette.client.actor_cookie()  helper function and pass it in  cookies=  like this: 
             @pytest.mark.asyncio
async def test_signed_cookie_actor():
    ds = Datasette(memory=True)
    cookies = {"ds_actor": ds.client.actor_cookie({"id": "root"})}
    response = await ds.client.get("/-/actor.json", cookies=cookies)
    assert response.json() == {"actor": {"id": "root"}}


id: testing_plugins:testing-plugins-pdb
page: testing_plugins
ref: testing-plugins-pdb
title: Using pdb for errors thrown inside Datasette
breadcrumbs: ["Testing plugins"]
references: []
content:
If an exception occurs within Datasette itself during a test, the response returned to your plugin will have a  response.status_code  value of 500. 
             You can add  pdb=True  to the  Datasette  constructor to drop into a Python debugger session inside your test run instead of getting back a 500 response code. This is equivalent to running the  datasette  command-line tool with the  --pdb  option. 
             Here's what that looks like in a test function: 
             def test_that_opens_the_debugger_or_errors():
    ds = Datasette([db_path], pdb=True)
    response = await ds.client.get("/") 
             If you use this pattern you will need to run  pytest  with the  -s  option to avoid capturing stdin/stdout in order to interact with the debugger prompt.


id: testing_plugins:testing-plugins-fixtures
page: testing_plugins
ref: testing-plugins-fixtures
title: Using pytest fixtures
breadcrumbs: ["Testing plugins"]
content:
Pytest fixtures  can be used to create initial testable objects which can then be used by multiple tests. 
             A common pattern for Datasette plugins is to create a fixture which sets up a temporary test database and wraps it in a Datasette instance. 
             Here's an example that uses the  sqlite-utils library  to populate a temporary test database. It also sets the title of that table using a simulated  metadata.json  configuration: 
             from datasette.app import Datasette
import pytest
import sqlite_utils


@pytest.fixture(scope="session")
def datasette(tmp_path_factory):
    db_directory = tmp_path_factory.mktemp("dbs")
    db_path = db_directory / "test.db"
    db = sqlite_utils.Database(db_path)
    db["dogs"].insert_all(
        [
            {"id": 1, "name": "Cleo", "age": 5},
            {"id": 2, "name": "Pancakes", "age": 4},
        ],
        pk="id",
    )
    datasette = Datasette(
        [db_path],
        metadata={
            "databases": {
                "test": {
                    "tables": {
                        "dogs": {"title": "Some dogs"}
                    }
                }
            }
        },
    )
    return datasette


@pytest.mark.asyncio
async def test_example_table_json(datasette):
    response = await datasette.client.get(
        "/test/dogs.json?_shape=array"
    )
    assert response.status_code == 200
    assert response.json() == [
        {"id": 1, "name": "Cleo", "age": 5},
        {"id": 2, "name": "Pancakes", "age": 4},
    ]


@pytest.mark.asyncio
async def test_example_table_html(datasette):
    response = await datasette.client.get("/test/dogs")
    assert ">Some dogs</h1>" in response.text 
             Here the  datasette()  function defines the fixture, which is than automatically passed to the two test functions based on pytest automatically matching their  datasette  function parameters. 
             The  @pytest.fixture(scope="session")  line here ensures the fixture is reused for the full  pytest  execution session. This means that the temporary database file will be created once and reused for each test. 
             If you want to create that test database repeatedly for every individual test function, write the fixture function like this instead. You may want to do this if your plugin modifies the database contents in some way: 
             @pytest.fixture
def datasette(tmp_path_factory):
    # This fixture will be executed repeatedly for every test
    ...

references:
[{"href": "https://docs.pytest.org/en/stable/fixture.html", "label": "Pytest fixtures"}, {"href": "https://sqlite-utils.datasette.io/en/stable/python-api.html", "label": "sqlite-utils library"}]


id: testing_plugins:testing-plugins-pytest-httpx
page: testing_plugins
ref: testing-plugins-pytest-httpx
title: Testing outbound HTTP calls with pytest-httpx
breadcrumbs: ["Testing plugins"]
references: [{"href": "https://pypi.org/project/pytest-httpx/", "label": "pytest-httpx"}]
content:
If your plugin makes outbound HTTP calls - for example datasette-auth-github or datasette-import-table - you may need to mock those HTTP requests in your tests. 
             The  pytest-httpx  package is a useful library for mocking calls. It can be tricky to use with Datasette though since it mocks all HTTPX requests, and Datasette's own testing mechanism uses HTTPX internally. 
             To avoid breaking your tests, you can return  ["localhost"]  from the  non_mocked_hosts()  fixture. 
             As an example, here's a very simple plugin which executes an HTTP response and returns the resulting content: 
             from datasette import hookimpl
from datasette.utils.asgi import Response
import httpx


@hookimpl
def register_routes():
    return [
        (r"^/-/fetch-url$", fetch_url),
    ]


async def fetch_url(datasette, request):
    if request.method == "GET":
        return Response.html(
            """
            <form action="/-/fetch-url" method="post">
            <input type="hidden" name="csrftoken" value="{}">
            <input name="url"><input type="submit">
        </form>""".format(
                request.scope["csrftoken"]()
            )
        )
    vars = await request.post_vars()
    url = vars["url"]
    return Response.text(httpx.get(url).text) 
             Here's a test for that plugin that mocks the HTTPX outbound request: 
             from datasette.app import Datasette
import pytest


@pytest.fixture
def non_mocked_hosts():
    # This ensures httpx-mock will not affect Datasette's own
    # httpx calls made in the tests by datasette.client:
    return ["localhost"]


async def test_outbound_http_call(httpx_mock):
    httpx_mock.add_response(
        url="https://www.example.com/",
        text="Hello world",
    )
    datasette = Datasette([], memory=True)
    response = await datasette.client.post(
        "/-/fetch-url",
        data={"url": "https://www.example.com/"},
    )
    assert response.text == "Hello world"

    outbound_request = httpx_mock.get_request()
    assert (
        outbound_request.url == "https://www.example.com/"
    )


id: testing_plugins:testing-plugins-register-in-test
page: testing_plugins
ref: testing-plugins-register-in-test
title: Registering a plugin for the duration of a test
breadcrumbs: ["Testing plugins"]
references: []
content:
When writing tests for plugins you may find it useful to register a test plugin just for the duration of a single test. You can do this using  pm.register()  and  pm.unregister()  like this: 
             from datasette import hookimpl
from datasette.app import Datasette
from datasette.plugins import pm
import pytest


@pytest.mark.asyncio
async def test_using_test_plugin():
    class TestPlugin:
        __name__ = "TestPlugin"

        # Use hookimpl and method names to register hooks
        @hookimpl
        def register_routes(self):
            return [
                (r"^/error$", lambda: 1 / 0),
            ]

    pm.register(TestPlugin(), name="undo")
    try:
        # The test implementation goes here
        datasette = Datasette()
        response = await datasette.client.get("/error")
        assert response.status_code == 500
    finally:
        pm.unregister(name="undo") 
             To reuse the same temporary plugin in multiple tests, you can register it inside a fixture in your  conftest.py  file like this: 
             from datasette import hookimpl
from datasette.app import Datasette
from datasette.plugins import pm
import pytest
import pytest_asyncio


@pytest_asyncio.fixture
async def datasette_with_plugin():
    class TestPlugin:
        __name__ = "TestPlugin"

        @hookimpl
        def register_routes(self):
            return [
                (r"^/error$", lambda: 1 / 0),
            ]

    pm.register(TestPlugin(), name="undo")
    try:
        yield Datasette()
    finally:
        pm.unregister(name="undo")
 
             Note the  yield  statement here - this ensures that the  finally:  block that unregisters the plugin is executed only after the test function itself has completed. 
             Then in a test: 
             @pytest.mark.asyncio
async def test_error(datasette_with_plugin):
    response = await datasette_with_plugin.client.get("/error")
    assert response.status_code == 500


id: internals:internals
page: internals
ref: internals
title: Internals for plugins
breadcrumbs: []
references: []
content:
Many  Plugin hooks  are passed objects that provide access to internal Datasette functionality. The interface to these objects should not be considered stable with the exception of methods that are documented here.


id: internals:internals-request
page: internals
ref: internals-request
title: Request object
breadcrumbs: ["Internals for plugins"]
content:
The request object is passed to various plugin hooks. It represents an incoming HTTP request. It has the following properties: 
             
                 
                     .scope  - dictionary 
                     
                         The ASGI scope that was used to construct this request, described in the  ASGI HTTP connection scope  specification. 
                     
                 
                 
                     .method  - string 
                     
                         The HTTP method for this request, usually  GET  or  POST . 
                     
                 
                 
                     .url  - string 
                     
                         The full URL for this request, e.g.  https://latest.datasette.io/fixtures . 
                     
                 
                 
                     .scheme  - string 
                     
                         The request scheme - usually  https  or  http . 
                     
                 
                 
                     .headers  - dictionary (str -> str) 
                     
                         A dictionary of incoming HTTP request headers. Header names have been converted to lowercase. 
                     
                 
                 
                     .cookies  - dictionary (str -> str) 
                     
                         A dictionary of incoming cookies 
                     
                 
                 
                     .host  - string 
                     
                         The host header from the incoming request, e.g.  latest.datasette.io  or  localhost . 
                     
                 
                 
                     .path  - string 
                     
                         The path of the request excluding the query string, e.g.  /fixtures . 
                     
                 
                 
                     .full_path  - string 
                     
                         The path of the request including the query string if one is present, e.g.  /fixtures?sql=select+sqlite_version() . 
                     
                 
                 
                     .query_string  - string 
                     
                         The query string component of the request, without the  ?  - e.g.  name__contains=sam&age__gt=10 . 
                     
                 
                 
                     .args  - MultiParams 
                     
                         An object representing the parsed query string parameters, see below. 
                     
                 
                 
                     .url_vars  - dictionary (str -> str) 
                     
                         Variables extracted from the URL path, if that path was defined using a regular expression. See  register_routes(datasette) . 
                     
                 
                 
                     .actor  - dictionary (str -> Any) or None 
                     
                         The currently authenticated actor (see  actors ), or  None  if the request is unauthenticated. 
                     
                 
             
             The object also has two awaitable methods: 
             
                 
                     await request.post_vars()  - dictionary 
                     
                         Returns a dictionary of form variables that were submitted in the request body via  POST . Don't forget to read about  CSRF protection ! 
                     
                 
                 
                     await request.post_body()  - bytes 
                     
                         Returns the un-parsed body of a request submitted by  POST  - useful for things like incoming JSON data. 
                     
                 
             
             And a class method that can be used to create fake request objects for use in tests: 
             
                 
                     fake(path_with_query_string, method="GET", scheme="http", url_vars=None) 
                     
                         Returns a  Request  instance for the specified path and method. For example: 
                         from datasette import Request
from pprint import pprint

request = Request.fake(
    "/fixtures/facetable/",
    url_vars={"database": "fixtures", "table": "facetable"},
)
pprint(request.scope) 
                         This outputs: 
                         {'http_version': '1.1',
 'method': 'GET',
 'path': '/fixtures/facetable/',
 'query_string': b'',
 'raw_path': b'/fixtures/facetable/',
 'scheme': 'http',
 'type': 'http',
 'url_route': {'kwargs': {'database': 'fixtures', 'table': 'facetable'}}}

references:
[{"href": "https://asgi.readthedocs.io/en/latest/specs/www.html#connection-scope", "label": "ASGI HTTP connection scope"}]


id: internals:internals-multiparams
page: internals
ref: internals-multiparams
title: The MultiParams class
breadcrumbs: ["Internals for plugins"]
references: []
content:
request.args  is a  MultiParams  object - a dictionary-like object which provides access to query string parameters that may have multiple values. 
             Consider the query string  ?foo=1&foo=2&bar=3  - with two values for  foo  and one value for  bar . 
             
                 
                     request.args[key]  - string 
                     
                         Returns the first value for that key, or raises a  KeyError  if the key is missing. For the above example  request.args["foo"]  would return  "1" . 
                     
                 
                 
                     request.args.get(key)  - string or None 
                     
                         Returns the first value for that key, or  None  if the key is missing. Pass a second argument to specify a different default, e.g.  q = request.args.get("q", "") . 
                     
                 
                 
                     request.args.getlist(key)  - list of strings 
                     
                         Returns the list of strings for that key.  request.args.getlist("foo")  would return  ["1", "2"]  in the above example.  request.args.getlist("bar")  would return  ["3"] . If the key is missing an empty list will be returned. 
                     
                 
                 
                     request.args.keys()  - list of strings 
                     
                         Returns the list of available keys - for the example this would be  ["foo", "bar"] . 
                     
                 
                 
                     key in request.args  - True or False 
                     
                         You can use  if key in request.args  to check if a key is present. 
                     
                 
                 
                     for key in request.args  - iterator 
                     
                         This lets you loop through every available key. 
                     
                 
                 
                     len(request.args)  - integer 
                     
                         Returns the number of keys.


id: internals:internals-response
page: internals
ref: internals-response
title: Response class
breadcrumbs: ["Internals for plugins"]
references: []
content:
The  Response  class can be returned from view functions that have been registered using the  register_routes(datasette)  hook. 
             The  Response()  constructor takes the following arguments: 
             
                 
                     body  - string 
                     
                         The body of the response. 
                     
                 
                 
                     status  - integer (optional) 
                     
                         The HTTP status - defaults to 200. 
                     
                 
                 
                     headers  - dictionary (optional) 
                     
                         A dictionary of extra HTTP headers, e.g.  {"x-hello": "world"} . 
                     
                 
                 
                     content_type  - string (optional) 
                     
                         The content-type for the response. Defaults to  text/plain . 
                     
                 
             
             For example: 
             from datasette.utils.asgi import Response

response = Response(
    "<xml>This is XML</xml>",
    content_type="application/xml; charset=utf-8",
) 
             The quickest way to create responses is using the  Response.text(...) ,  Response.html(...) ,  Response.json(...)  or  Response.redirect(...)  helper methods: 
             from datasette.utils.asgi import Response

html_response = Response.html("This is HTML")
json_response = Response.json({"this_is": "json"})
text_response = Response.text(
    "This will become utf-8 encoded text"
)
# Redirects are served as 302, unless you pass status=301:
redirect_response = Response.redirect(
    "https://latest.datasette.io/"
) 
             Each of these responses will use the correct corresponding content-type -  text/html; charset=utf-8 ,  application/json; charset=utf-8  or  text/plain; charset=utf-8  respectively. 
             Each of the helper methods take optional  status=  and  headers=  arguments, documented above.


id: internals:internals-response-asgi-send
page: internals
ref: internals-response-asgi-send
title: Returning a response with .asgi_send(send)
breadcrumbs: ["Internals for plugins", "Response class"]
references: []
content:
In most cases you will return  Response  objects from your own view functions. You can also use a  Response  instance to respond at a lower level via ASGI, for example if you are writing code that uses the  asgi_wrapper(datasette)  hook. 
                 Create a  Response  object and then use  await response.asgi_send(send) , passing the ASGI  send  function. For example: 
                 async def require_authorization(scope, receive, send):
    response = Response.text(
        "401 Authorization Required",
        headers={
            "www-authenticate": 'Basic realm="Datasette", charset="UTF-8"'
        },
        status=401,
    )
    await response.asgi_send(send)


id: internals:internals-response-set-cookie
page: internals
ref: internals-response-set-cookie
title: Setting cookies with response.set_cookie()
breadcrumbs: ["Internals for plugins", "Response class"]
references: []
content:
To set cookies on the response, use the  response.set_cookie(...)  method. The method signature looks like this: 
                 def set_cookie(
    self,
    key,
    value="",
    max_age=None,
    expires=None,
    path="/",
    domain=None,
    secure=False,
    httponly=False,
    samesite="lax",
): ... 
                 You can use this with  datasette.sign()  to set signed cookies. Here's how you would set the  ds_actor cookie  for use with Datasette  authentication : 
                 response = Response.redirect("/")
response.set_cookie(
    "ds_actor",
    datasette.sign({"a": {"id": "cleopaws"}}, "actor"),
)
return response


id: internals:internals-datasette
page: internals
ref: internals-datasette
title: Datasette class
breadcrumbs: ["Internals for plugins"]
references: []
content:
This object is an instance of the  Datasette  class, passed to many plugin hooks as an argument called  datasette . 
             You can create your own instance of this - for example to help write tests for a plugin - like so: 
             from datasette.app import Datasette

# With no arguments a single in-memory database will be attached
datasette = Datasette()

# The files= argument can load files from disk
datasette = Datasette(files=["/path/to/my-database.db"])

# Pass metadata as a JSON dictionary like this
datasette = Datasette(
    files=["/path/to/my-database.db"],
    metadata={
        "databases": {
            "my-database": {
                "description": "This is my database"
            }
        }
    },
) 
             Constructor parameters include: 
             
                 
                     files=[...]  - a list of database files to open 
                 
                 
                     immutables=[...]  - a list of database files to open in immutable mode 
                 
                 
                     metadata={...}  - a dictionary of  Metadata 
                 
                 
                     config_dir=...  - the  configuration directory  to use, stored in  datasette.config_dir


id: internals:datasette-databases
page: internals
ref: datasette-databases
title: .databases
breadcrumbs: ["Internals for plugins", "Datasette class"]
references: []
content:
Property exposing a  collections.OrderedDict  of databases currently connected to Datasette. 
                 The dictionary keys are the name of the database that is used in the URL - e.g.  /fixtures  would have a key of  "fixtures" . The values are  Database class  instances. 
                 All databases are listed, irrespective of user permissions.


id: internals:datasette-permissions
page: internals
ref: datasette-permissions
title: .permissions
breadcrumbs: ["Internals for plugins", "Datasette class"]
references: []
content:
Property exposing a dictionary of permissions that have been registered using the  register_permissions(datasette)  plugin hook. 
                 The dictionary keys are the permission names - e.g.  view-instance  - and the values are  Permission()  objects describing the permission. Here is a  description of that object .


id: internals:datasette-plugin-config
page: internals
ref: datasette-plugin-config
title: .plugin_config(plugin_name, database=None, table=None)
breadcrumbs: ["Internals for plugins", "Datasette class"]
references: []
content:
plugin_name  - string 
                         
                             The name of the plugin to look up configuration for. Usually this is something similar to  datasette-cluster-map . 
                         
                     
                     
                         database  - None or string 
                         
                             The database the user is interacting with. 
                         
                     
                     
                         table  - None or string 
                         
                             The table the user is interacting with. 
                         
                     
                 
                 This method lets you read plugin configuration values that were set in   datasette.yaml . See  Writing plugins that accept configuration  for full details of how this method should be used. 
                 The return value will be the value from the configuration file - usually a dictionary. 
                 If the plugin is not configured the return value will be  None .


id: internals:datasette-render-template
page: internals
ref: datasette-render-template
title: await .render_template(template, context=None, request=None)
breadcrumbs: ["Internals for plugins", "Datasette class"]
content:
template  - string, list of strings or jinja2.Template 
                         
                             The template file to be rendered, e.g.  my_plugin.html . Datasette will search for this file first in the  --template-dir=  location, if it was specified - then in the plugin's bundled templates and finally in Datasette's set of default templates. 
                             If this is a list of template file names then the first one that exists will be loaded and rendered. 
                             If this is a Jinja  Template object  it will be used directly. 
                         
                     
                     
                         context  - None or a Python dictionary 
                         
                             The context variables to pass to the template. 
                         
                     
                     
                         request  - request object or None 
                         
                             If you pass a Datasette request object here it will be made available to the template. 
                         
                     
                 
                 Renders a  Jinja template  using Datasette's preconfigured instance of Jinja and returns the resulting string. The template will have access to Datasette's default template functions and any functions that have been made available by other plugins.

references:
[{"href": "https://jinja.palletsprojects.com/en/2.11.x/api/#jinja2.Template", "label": "Template object"}, {"href": "https://jinja.palletsprojects.com/en/2.11.x/", "label": "Jinja template"}]


id: internals:datasette-actors-from-ids
page: internals
ref: datasette-actors-from-ids
title: await .actors_from_ids(actor_ids)
breadcrumbs: ["Internals for plugins", "Datasette class"]
references: []
content:
actor_ids  - list of strings or integers 
                         
                             A list of actor IDs to look up. 
                         
                     
                 
                 Returns a dictionary, where the keys are the IDs passed to it and the values are the corresponding actor dictionaries. 
                 This method is mainly designed to be used with plugins. See the  actors_from_ids(datasette, actor_ids)  documentation for details. 
                 If no plugins that implement that hook are installed, the default return value looks like this: 
                 {
    "1": {"id": "1"},
    "2": {"id": "2"}
}


id: internals:datasette-permission-allowed
page: internals
ref: datasette-permission-allowed
title: await .permission_allowed(actor, action, resource=None, default=...)
breadcrumbs: ["Internals for plugins", "Datasette class"]
references: []
content:
actor  - dictionary 
                         
                             The authenticated actor. This is usually  request.actor . 
                         
                     
                     
                         action  - string 
                         
                             The name of the action that is being permission checked. 
                         
                     
                     
                         resource  - string or tuple, optional 
                         
                             The resource, e.g. the name of the database, or a tuple of two strings containing the name of the database and the name of the table. Only some permissions apply to a resource. 
                         
                     
                     
                         default  - optional: True, False or None 
                         
                             What value should be returned by default if nothing provides an opinion on this permission check.
                                Set to  True  for default allow or  False  for default deny.
                                If not specified the  default  from the  Permission()  tuple that was registered using  register_permissions(datasette)  will be used. 
                         
                     
                 
                 Check if the given actor has  permission  to perform the given action on the given resource. 
                 Some permission checks are carried out against  rules defined in datasette.yaml , while other custom permissions may be decided by plugins that implement the  permission_allowed(datasette, actor, action, resource)  plugin hook. 
                 If neither  metadata.json  nor any of the plugins provide an answer to the permission query the  default  argument will be returned. 
                 See  Built-in permissions  for a full list of permission actions included in Datasette core.


id: internals:datasette-ensure-permissions
page: internals
ref: datasette-ensure-permissions
title: await .ensure_permissions(actor, permissions)
breadcrumbs: ["Internals for plugins", "Datasette class"]
references: []
content:
actor  - dictionary 
                         
                             The authenticated actor. This is usually  request.actor . 
                         
                     
                     
                         permissions  - list 
                         
                             A list of permissions to check. Each permission in that list can be a string  action  name or a 2-tuple of  (action, resource) . 
                         
                     
                 
                 This method allows multiple permissions to be checked at once. It raises a  datasette.Forbidden  exception if any of the checks are denied before one of them is explicitly granted. 
                 This is useful when you need to check multiple permissions at once. For example, an actor should be able to view a table if either one of the following checks returns  True  or not a single one of them returns  False : 
                 await datasette.ensure_permissions(
    request.actor,
    [
        ("view-table", (database, table)),
        ("view-database", database),
        "view-instance",
    ],
)


id: internals:datasette-check-visibility
page: internals
ref: datasette-check-visibility
title: await .check_visibility(actor, action=None, resource=None, permissions=None)
breadcrumbs: ["Internals for plugins", "Datasette class"]
references: []
content:
actor  - dictionary 
                         
                             The authenticated actor. This is usually  request.actor . 
                         
                     
                     
                         action  - string, optional 
                         
                             The name of the action that is being permission checked. 
                         
                     
                     
                         resource  - string or tuple, optional 
                         
                             The resource, e.g. the name of the database, or a tuple of two strings containing the name of the database and the name of the table. Only some permissions apply to a resource. 
                         
                     
                     
                         permissions  - list of  action  strings or  (action, resource)  tuples, optional 
                         
                             Provide this instead of  action  and  resource  to check multiple permissions at once. 
                         
                     
                 
                 This convenience method can be used to answer the question "should this item be considered private, in that it is visible to me but it is not visible to anonymous users?" 
                 It returns a tuple of two booleans,  (visible, private) .  visible  indicates if the actor can see this resource.  private  will be  True  if an anonymous user would not be able to view the resource. 
                 This example checks if the user can access a specific table, and sets  private  so that a padlock icon can later be displayed: 
                 visible, private = await datasette.check_visibility(
    request.actor,
    action="view-table",
    resource=(database, table),
) 
                 The following example runs three checks in a row, similar to  await .ensure_permissions(actor, permissions) . If any of the checks are denied before one of them is explicitly granted then  visible  will be  False .  private  will be  True  if an anonymous user would not be able to view the resource. 
                 visible, private = await datasette.check_visibility(
    request.actor,
    permissions=[
        ("view-table", (database, table)),
        ("view-database", database),
        "view-instance",
    ],
)


